EXtreme-scale Analytics via Multimodal Ontology Discovery & Enhancement

Risultati finali

Algorithms for whole-slide image compression

Algorithms for wholeslide image compression The input of those algorithm is a multiresolution Gigapixel histopathology wholeslide image and the output is a compressed representation of the input in the form of a data volume rows columns features The amount of rows and column of the compressed representation will be much smaller than the original wholeslide image and the number of features will be in the order of 128 or 256 values Encoders will be implemented in the form of a webbased service in the EXA MODE web site which will allow to upload WSIs and download a compressed representation of it in the form of a data fileThe tool is tested in order to verify Milestone 8

Conceptual descriptive framework for multimodal knowledge

This designed conceptual descriptive framework allows medical computer science experts to easily represent into a unified framework multimodal medical information reducing the effort to combine textual and imaging data for clinical decision support systems

2nd dissemination, communication and exploitation report & outline for the following year

This report reviews all the dissemination exploitation and communication activities performed during the year and it draws the outlines for the following year divided per activity

Graph representation of the histopathology knowledge

The histopathology visual knowledge graph represents the relationships between scale and color invariant content of the histopathology images It is described in a scientific publication and it is released to the other members of the consortium

Algorithms for whole-slide image classification

Deep neural networks for classification of histopathology wholeslide images Targets of trained networks will be released as a milestone during the project Classification algorithms will be implemented in a webbased platform available at Radboudumc namely CIRRUS Pathology Techniques addressing the problem of endtoend training of neural networks for WSI classification will be disseminated as scientific publicationsThe tool is tested in order to verify Milestone 8

Semantic knowledge extractor prototype

Description This designed semantic knowledge extractor allows to represent textual descriptions of medical reports in terms of semantic networks extracting authoritative concepts and semantic relations out of text The released prototype benefits from the feedbacks of clinicians that supervised the quality of the resultsThe semantic knowledge extractor prototype includes the preliminary version of the visualisation tool prototype with basic functionalities targeting internal use in order to verify Milestone 7

Multimodal knowledge representation originating from images

The multimodal knowledge graphs includes paired histopathology visual and text information It is released to the other members of the consortium and it empowers WP 4 and WP5

Final dissemination, communication and exploitation report

This report reviews all the dissemination exploitation and communication activities performed during the entire project It also provides a plan for continued dissemination activities subsequent to the project The deliverable includes the planned exploitation activities which are expected to take place upon the end of the project

Data and model parallel approaches for training ANNs

Efficient scaling algorithms We focus on the large batch size learning challenge scaling behavior and hardware efficiency Techniques addressing the problem of scaling up the endtoend training of neural networks for WSI classification will be disseminated as scientific publications

1st dissemination, communication and exploitation report & outline for the following year

This report reviews all the dissemination, exploitation and communication activities performed during the year and it draws the outlines for the following year, divided per activity.

Algorithms for semantic segmentation and detection in histopathology images

Deep neural networks for detection and semantic segmentation of tissue regions in whole-slide images. Targets of trained networks will be released as a milestone during the project. Detection and segmentation algorithms will be implemented in a web-based platform available at Radboudumc, namely CIRRUS Pathology. Segmentation results will be produced in a format compatible with the in-house developed open-source platform ASAP [61], as well as CIRRUS Pathology, which will allow to inspect, modify and further process segmentation results as well as likelihood maps produced by neural networks. Novel techniques of weakly-supervised semantic in whole-slide images will be disseminated as scientific publications. The tool is tested in order to verify Milestone 8.

Automatic knowledge discovery system prototype and user study outcome

This knowledge discovery system allows clinicians to automatically obtain connected relevant information in different combinations The returned types of information are report textual descriptions medical images authoritative concepts and semantic relations between them The released prototype take into account the different heterogeneous components of the systems and benefit from the feedbacks of expert users that tested the tool in the field The deliverable is shared among UNIPD and HESSO because they will strongly collaborate on the prototype

Final set of annotated digital pathology data

The final set of annotated whole slide images are made available to the consortium. Such data are selected from the AOEC data and must include at least 100 annotated whole slide images in the final annotated dataset. The annotation requirements are defined by AOEC and MICROSCOPEIT together before the data annotation begins and are made available on the private section of the EXA MODE web page. The data annotations are performed using a software based on previous works of the partners HES-SO and MICROSCOPEIT.

Final set of data curated and available

The final set of whole slide images and medical report are made available to the consortium. Such data are selected from the 600’000 AOEC and Radboudumc data according to the consortium requirements.

First set of annotated digital pathology data

The first set of annotated whole slide images are made available to the consortium. Such data are selected from the AOEC data and must include at least 100 annotated whole slide images in the final annotated dataset. The annotation requirements are defined by AOEC and MICROSCOPEIT together before the data annotation begins and are made available on the private section of the EXA MODE web page. The data annotations are performed using a software based on previous works of the partners HES-SO and MICROSCOPEIT.

First set of cured, publicly available multimodal and multimedia data

The first set of cured publicly available data include respectively at least 500 and 5’000 histopathology images and related text extracted from scientific literature and the web. The images and the related text include content that is relevant for histopathology diagnostic purposes and that can be used to train machine learning based algorithms.

Set of publicly available algorithms to separate compound images

The algorithms to separate compound images and link them to related text are presented into a scientific publication and they are publicly released on the EXA MODE website.

Tools to extract homogeneous representations of heterogeneous colour visual information

The algorithms and tools are presented into a scientific publication and they are publicly released on the EXA MODE websiteThe prototypes are tested in order to verify Milestone 6

Tools to extract multi-scale representations of visual information

The algorithms and tools to extract multiscale representations of visual information are presented into a scientific publication and they are publicly released on the EXA MODE websiteThe prototypes are tested in order to verify Milestone 6

Final set of cured, publicly available multimodal and multimedia data

The final set of cured publicly available data include respectively at least 500 and 5’000 histopathology images and related text extracted from scientific literature and the web. The images and the related text include content that is relevant for histopathology diagnostic purposes and that can be used to train machine learning based algorithms.

First set of data curated and available

The first set of whole slide images and medical report are made available to the consortium. Such data are selected from the 600’000 AOEC and Radboudumc data according to the consortium requirements.

Dynamic visual analytics prototype and user study outcome

This visualization analytics prototype allows the interaction with the learning methods and allows the experts to understand intermediate learning states. The released prototype take into account the feedbacks of expert users that tested the tool in the field.

EXA MODE website

The web site contains the description of the project, a section with a list of publications (and also press releases and articles in the popular press) and a private section for the partners to exchange software, data and information. All public deliverables are available as pdf from the web page. The web site includes an RSS feeder with news on the project and it is updated regularly.

Pubblicazioni

Stainlib: a python library for augmentation and normalization of histopathology H&E images.

Autori: Sebastian Otálora, Niccolò Marini, Damian Podareanu, Ruben Hekster, David Tellez, Jeroen Van Der Laak, Henning Müller, Manfredo Atzori
Pubblicato in: 2022
Editore: BiorXiv

Theory and Practice of Digital Libraries: 26th International Conference on Theory and Practice of Digital Librarie

Autori: Silvello, G., Corcho, O., Manghi, P., Di Nunzio, G. M., Golub, K., Ferro, N., & Poggi, A. (Eds.). Linking s, TPDL 2022, Padua, Italy, September 20–23, 2022, Proceedings (Vol. 13541).
Pubblicato in: 2022
Editore: Springer Nature

Knowledge Representation and Language Simplification of Human Rights

Autori: Silecchia, S., Vezzani, F., & Di Nunzio, G. M.
Pubblicato in: Proceedings of the Workshop on Terminology in the 21st century: many faces, many places, Numero pp. 8-12, 2022
Editore: ACL

A Post-Analysis of Query Reformulation Methods for Clinical Trials Retrieval

Autori: Agosti, Maristella, Giorgio Maria Di Nunzio, and Stefano Marchesin
Pubblicato in: SEBD, 2020
Editore: Ceur-Ws

Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations

Autori: Niccolò Marini, Sebastian Otálora, Francesco Ciompi, Gianmaria Silvello, Stefano Marchesin, Simona Vatrano, Genziana Buttafuoco, Manfredo Atzori, Henning Müller
Pubblicato in: MICCAI Workshop on Computational Pathology, 2021
Editore: Proceedings of Machine Learning Research

Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification

Autori: Hristov, Anton, Aleksandar Tahchiev, Hristo Papazov, Nikola Tulechki, Todor Primov, and Svetla Boytcheva
Pubblicato in: International Conference on Recent Advances in Natural Language Processing (RANLP 2021), Numero 1.10.2021, 2021, Pagina/e 557-565, ISBN 978-954-452-072-4
Editore: INCOMA Ltd.
DOI: 10.26615/978-954-452-072-4_063

DocTAG: A Customizable Annotation Tool for Ground Truth Creation

Autori: Giachelle, F., Irrera, O., & Silvello, G.
Pubblicato in: . In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 2022, Pagina/e (pp. 288-293)
Editore: ham: Springer International Publishing
DOI: 10.1007/978-3-030-99739-7_35

Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations

Autori: Niccolo Marini, Sebastian Otalora, Henning Muller, and Manfredo Atzori
Pubblicato in: International Workshop on Artificial Intelligence for Digital Pathology, International Conference on Pattern Recognition (ICPR), 2021
Editore: Springer

Data Credit Distribution through Lineage (Extended Abstract)

Autori: Dennis Dosso and Gianmaria Silvello
Pubblicato in: Proc. of the 17th Italian Research Conference on Digital Libraries (IRCDL 2021), 2021
Editore: Ceur-WS Proceedings

Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images

Autori: W. Aswolinskiy, D. Tellez, G. Raya, L. van der Woude, M. Looijen-Salamon, J. van der Laak, K. Grunberg and F. Ciompi
Pubblicato in: 2021
Editore: SPIE Medical Imaging

Searching for Reliable Facts over a Medical Knowledge Base (demo)

Autori: Fabio Giachelle, Stefano Marchesin, Gianmaria Silvello, and Omar Alonso
Pubblicato in: Proc. of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), 2023, Pagina/e pages 3205–3209
Editore: ACM
DOI: 10.1145/3539618.3591822

Sharpening Local Interpretable Model-Agnostic Explanations for Histopathology: Improved Understandability and Reliability

Autori: Graziani, Mara, Iam Palatnik de Sousa, Marley MBR Vellasco, Eduardo Costa da Silva, Henning Müller, and Vincent Andrearczyk
Pubblicato in: 2021
Editore: Springer LCNS
DOI: 10.1007/978-3-030-87199-4_51

A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022

Autori: Niccolò Marini, Marek Wodzinski, Manfredo Atzori, Henning Mueller
Pubblicato in: IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), 2022
Editore: IEEE
DOI: 10.1109/isbic56247.2022.9854527

Neural Feature Selection for Learning to Rank

Autori: Purpura, Alberto, Karolina Buchner, Gianmaria Silvello, and Gian Antonio Susto
Pubblicato in: Advances in Information Retrieval, ECIR 2021, 2021
Editore: Springer
DOI: 10.1007/978-3-030-72240-1_34

Few-shot weakly supervised detection and retrieval in histopathology whole-slide images

Autori: M. van Rijthoven, M. Balkenhol, M. Atzori, P. Bult, J. van der Laak and F. Ciompi
Pubblicato in: 2021
Editore: SPIE Medical Imaging

Knowledge Enhanced Representations to Reduce the Semantic Gap in Clinical Decision Support

Autori: MARCHESIN, STEFANO
Pubblicato in: Numero 1, 2019
Editore: CEUR-WS

What Makes a Query Semantically Hard?

Autori: G. Faggioli, S. Marchesin
Pubblicato in: Proc. of the 2nd International conference on DESIRES, 2021
Editore: Ceur-Ws Proceedings

On the Formal Standardization of Terminology Resources: The Case Study of TriMED

Autori: Vezzani, Federica; Di Nunzio, Giorgio Maria
Pubblicato in: Numero 1, 2020
Editore: European Language Resources Association

H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression

Autori: Niccolo Marini, Manfredo Atzori, Sebastian Otálora, Stephane Marchand-Maillet, Henning Müller
Pubblicato in: IEEE/CVF International Conference on Computer Vision, 2021
Editore: IEEE

Exploring how to Combine Query Reformulations for Precision Medicine

Autori: DI NUNZIO, GIORGIO MARIA; MARCHESIN, STEFANO; AGOSTI, MARISTELLA
Pubblicato in: Numero 1, 2019
Editore: NIST

Expanding the Citation Graph for Data Citations

Autori: Buneman, P., Dosso, D., Lissandrini, M., & Silvello, G.
Pubblicato in: In CEUR Workshop Proceedings (Vol. 3194), 2022, Pagina/e pp. 276-283
Editore: CEUR Workshop Proceedings

Pairwise fairness in ranking as a dissatisfaction measure.

Autori: Fabris, A., Silvello, G., Susto, G. A., & Biega, A. J.
Pubblicato in: In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 931-939)., 2023
Editore: ACM
DOI: 10.1145/3539597.3570459

SAFIR: a Semantic-Aware Neural Framework for IR

Autori: M. Agosti, S. Marchesin, and G. Silvello
Pubblicato in: Proceedings of the 11th Italian Information Retrieval Workshop 2021, 2021
Editore: Ceur-Ws

Incentives for Item Duplication under Fair Ranking Policies

Autori: Giorgio Maria Di Nunzio, Alessandro Fabris, Gianmaria Silvello and Gian Antonio Susto
Pubblicato in: Proc. of the 2nd International Workshop on Algorithmic Bias in Search and Recommendation (BIAS@ECIR2021), 2021
Editore: Springer
DOI: 10.1007/978-3-030-78818-6

Building a Relation Extraction Baseline for Gene-Disease Associations: A Reproducibility Study

Autori: Menotti, L.
Pubblicato in: ESSIR 2022, 2022
Editore: Online

A scalable virtual document-based keyword search system for RDF datasets

Autori: Dennis Dosso and Gianmaria Silvello
Pubblicato in: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Numero 7, 2019
Editore: ACM Press
DOI: 10.1145/3331184.3331284

Knowledge Enhanced Representations for Clinical Decision Support

Autori: MARCHESIN, STEFANO; AGOSTI, MARISTELLA
Pubblicato in: Numero 1, 2019
Editore: CEUR-WS

Multimodal latent semantic alignment for automated prostate tissue classification and retrieval

Autori: Lara, Juan S., Victor H. Contreras O, Sebastián Otálora, Henning Müller, and Fabio A. González
Pubblicato in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020
Editore: Springer LCNS
DOI: 10.1007/978-3-030-59722-1_55

SKET: an Unsupervised Knowledge Extraction Tool to Empower Digital Pathology Applications

Autori: Di Nunzio, G. M., Ferro, N., Giachelle, F., Irrera, O., Marchesin, S., & Silvello, G.
Pubblicato in: CEUR WORKSHOP PROCEEDINGS, IIR 2023, Vol. 3365, 2023, Pagina/e pp. 144-152).
Editore: CUER Ws

As Simple as Possible: Using the R Tidyverse for Multilingual Information Extraction

Autori: Di Nunzio, Giorgio Maria
Pubblicato in: CLEF eHealth 2020, Numero 23.10.2020, 2020
Editore: Ceur-Ws

Classification of noisy free-text prostate cancer pathology reports using natural language processing

Autori: Anjani Dhrangadhariya, Sebastian Otálora, Manfredo Atzori, and Henning Muller.
Pubblicato in: International Workshop on Artificial Intelligence for Digital Pathology, International Conference on Pattern Recognition (ICPR), 2021
Editore: Springer

Query Performance Prediction for Neural IR: Are We There Yet?

Autori: Faggioli, Guglielmo; Formal, Thibault; Marchesin, Stefano; Clinchant, Stéphane; Ferro, Nicola; Piwowarski, Benjamin
Pubblicato in: Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Numero 19, 2023
Editore: Springer
DOI: 10.1007/978-3-031-28244-7_15

Terminology Extraction in Electronic Health Records. The ExaMode Project.

Autori: Marchesin, S., Di Nunzio, G. M., & Silvello, G.
Pubblicato in: 2022
Editore: CUER Ws

Generalizing convolution neural networks on stain color heterogeneous data for computational pathology

Autori: Amjad Khan, Manfredo Atzori, Sebastian Otálora, Vincent Andrearczyk, Henning Müller
Pubblicato in: Medical Imaging 2020: Digital Pathology, 2020, Pagina/e 26, ISBN 9781-510634084
Editore: SPIE
DOI: 10.1117/12.2549718

A systematic comparison of deep learning strategies for weakly supervised Gleason grading

Autori: Sebastian Otálora, Manfredo Atzori, Amjad Khan, Oscar Jimenez-del-Toro, Vincent Andrearczyk, Henning Müller
Pubblicato in: Medical Imaging 2020: Digital Pathology, 2020, Pagina/e 20, ISBN 9781-510634084
Editore: SPIE
DOI: 10.1117/12.2548571

Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies

Autori: Anjani K. Dhrangadhariya, Oscar Jimenez-del-Toro, Vincent Andrearczyk, Manfredo Atzori, Henning Müller
Pubblicato in: Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 2020, Pagina/e 9, ISBN 9781-510634046
Editore: SPIE
DOI: 10.1117/12.2549565

An Analysis of Query Reformulation Techniques for Precision Medicine

Autori: Maristella Agosti, Giorgio Maria Di Nunzio, Stefano Marchesin
Pubblicato in: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, Pagina/e 973-976, ISBN 9781-450361729
Editore: ACM
DOI: 10.1145/3331184.3331289

Probabilistic Word Embeddings in Neural IR - A Promising Model That Does Not Work as Expected (For Now)

Autori: Alberto Purpura, Marco Maggipinto, Gianmaria Silvello, Gian Antonio Susto
Pubblicato in: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, 2019, Pagina/e 3-10, ISBN 9781-450368810
Editore: ACM
DOI: 10.1145/3341981.3344217

Medical Retrieval using Structured Information Extracted from Knowledge Bases

Autori: Agosti, M.; Di Nunzio, G. M.; Marchesin, S.; Gianmaria Silvello
Pubblicato in: Scopus - Elsevier, Numero 1, 2019
Editore: CEUR-WS

CoreKB: A Web-based Platform for Searching Reliable Facts over a Medical Knowledge Base

Autori: Giachelle, F., Marchesin, S., Silvello, G., & Alonso, O.
Pubblicato in: SEBD 2023, 2023
Editore: Ceur-Ws

A Study on Reciprocal Ranking Fusion in Consumer Health Search

Autori: Di Nunzio, Giorgio Maria, Stefano Marchesin, and Federica Vezzani
Pubblicato in: CLEF eHealth 2020 Task 2, Numero 23.10.2020, 2020
Editore: Ceur-Ws

A Bayesian Neural Model for Documents' Relevance Estimation

Autori: Purpura, Alberto, and Gian Antonio Susto
Pubblicato in: """2nd International Conference on Design of Experimental Search Information REtrieval Systems,""", Numero 15.10.2021, 2021
Editore: Ceur-Ws

A Keyword Search and Citation System for RDF Graphs

Autori: Dosso, Dennis
Pubblicato in: FDIA@ ESSIR, Numero 17.07.2019, 2019
Editore: Ceur-Ws

Learning from sparsely annotated data for semantic segmentation in histopathology images

Autori: J.-M. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi
Pubblicato in: Proceedings of Machine Learning Research, Numero Volume 102, 2019, Pagina/e 81-94
Editore: PMLR

An Ontology-Driven Knowledge Extraction Tool for Pathology Record Classification.

Autori: Menotti, L., Marchesin, S., & Silvello, G.
Pubblicato in: 2023
Editore: Ceur-Ws

Data Search in practice: How to find scientific datasets and to link them to the literature.

Autori: Irrera, O.
Pubblicato in: 2022
Editore: Online procs

Nanocitation: Complete and Interoperable Citations of Nanopublications

Autori: Fabris, Erika, Tobias Kuhn, and Gianmaria Silvello
Pubblicato in: Italian Research Conference on Digital Libraries, Numero 01.2020, 2020
Editore: Springer
DOI: 10.1007/978-3-030-39905-4_18

NanoWeb: Search, Access and Explore Life Science Nanopublications on the Web (Extended Abstract)

Autori: Fabio Giachelle, Dennis Dosso and Gianmaria Silvello
Pubblicato in: Proc. 29th Italian Symposium on Advanced Database Systems (SEBD 2021), 2021
Editore: Ceur-Ws

Semi-weakly supervised learning for prostate cancer image classification with teacher-student deep convolutional networks

Autori: Otálora, Sebastian, Niccolo Marini, Henning Müller, and Manfredo Atzori
Pubblicato in: 2020
Editore: Lecture Notes in Computer Science book series
DOI: 10.1007/978-3-030-61166-8_21

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

Autori: Tellez, David; Hoppener, Diederik; Verhoef, Cornelis; Grunhagen, Dirk; Nierop, Pieter; Drozdzal, Michal; van der Laak, Jeroen; Ciompi, Francesco
Pubblicato in: Extending Unsupervised Neural Image Compression With Supervised Multitask Learning, 2020
Editore: PMLR

Background linking: Joining entity linking with learning to rank models

Autori: Irrera, O.; Silvello, G.
Pubblicato in: Proc. of the 17th Italian Research Conference on Digital Libraries (IRCDL 2021), Numero 1, 2021
Editore: Ceur-WS Proceedings

Credit distribution in relational scientific databases

Autori: Dennis Dosso; Susan B. Davidson; Gianmaria Silvello
Pubblicato in: Information Systems, Numero 6, 2022, ISSN 0306-4379
Editore: Elsevier Science & Technology
DOI: 10.1016/j.is.2022.102060

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Autori: David Tellez, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, Jeroen van der Laak
Pubblicato in: Medical Image Analysis, Numero 58, 2019, Pagina/e 101544, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2019.101544

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer

Autori: Bokhorst, J. M., Ciompi, F., Öztürk, S. K., Erdogan, A. S. O., Vieth, M., Dawson, H., Kirsch, R., Simmer, F., Sheahan, K., Lugli, A., Zlobec, I., van der Laak, J., & Nagtegaal, I. D
Pubblicato in: Modern pathology, 2023, ISSN 0893-3952
Editore: Nature Publishing Group
DOI: 10.1016/j.modpat.2023.100233

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification. BMC Medical Imaging.

Autori: Sebastian Otálora, Niccolo Marini, Henning Müller, and Manfredo Atzori
Pubblicato in: BMC Medical Imaging, 2021, ISSN 1471-2342
Editore: BioMed Central
DOI: 10.1186/s12880-021-00609-0

Methodology for the standardization of terminological resources: Design of TriMED database to support multi-register medical communication

Autori: Vezzani, Federica, and Giorgio Maria Di Nunzio
Pubblicato in: Terminology. International Journal of Theoretical and Applied Numeros in Specialized Communication, Numero 26, 2020, ISSN 0929-9971
Editore: John Benjamins Publishing Company
DOI: 10.1075/term.00053.vez

Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval

Autori: Maristella Agosti; Stefano Marchesin; Gianmaria Silvello
Pubblicato in: ACM TOIS, Numero 2, 2020, ISSN 1046-8188
Editore: Association for Computing Machinary, Inc.
DOI: 10.1145/3417996

Empowering digital pathology applications through explainable knowledge extraction tools

Autori: Stefano Marchesin; Fabio Giachelle; Niccolò Marini; Manfredo Atzori; Svetla Boytcheva; Genziana Buttafuoco; Francesco Ciompi; Giorgio Maria Di Nunzio; Filippo Fraggetta; Ornella Irrera; Henning Müller; Todor Primov; Simona Vatrano; Gianmaria Silvello
Pubblicato in: urn:issn:2153-3539, Numero 21, 2022, ISSN 2153-3539
Editore: Elsevier
DOI: 10.1016/j.jpi.2022.100139

A Novel Curated Scholarly Graph Connecting Textual and Data Publications

Autori: Ornella Irrera, Andrea Mannocci, Paolo Manghi , Gianmaria Silvello
Pubblicato in: Journal of Data and Information Quality, 2023, ISSN 1936-1955
Editore: Association for Computing Machinary, Inc.
DOI: 10.1145/3597310

Search, access, and explore life science nanopublications on the Web

Autori: Fabio Giachelle; Dennis Dosso; Gianmaria Silvello
Pubblicato in: PeerJ Computer Science, Numero 1, 2021, ISSN 2376-5992
Editore: PeerJ Inc.
DOI: 10.7717/peerj-cs.335

Neural Image Compression for Gigapixel Histopathology Image Analysis

Autori: David Tellez, Geert Litjens, Jeroen van der Laak, Francesco Ciompi
Pubblicato in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, Pagina/e 1-1, ISSN 0162-8828
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2019.2936841

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification

Autori: Niccolò Marini; Sebastian Otálora; Henning Müller; Manfredo Atzori
Pubblicato in: Crossref, Numero 1, 2021, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2021.102165

Learning to detect lymphocytes in immunohistochemistry with deep learning

Autori: Zaneta Swiderska-Chadaj, Hans Pinckaers, Mart van Rijthoven, Maschenka Balkenhol, Margarita Melnikova, Oscar Geessink, Quirine Manson, Mark Sherman, Antonio Polonia, Jeremy Parry, Mustapha Abubakar, Geert Litjens, Jeroen van der Laak, Francesco Ciompi
Pubblicato in: Medical Image Analysis, Numero 58, 2019, Pagina/e 101547, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2019.101547

Data Citation and the Citation Graph, accepted to Quantitative Social Sciences

Autori: Peter Buneman, Dennis Dosso, Matteo Lissandrini, Gianmaria Silvello
Pubblicato in: Quantitative Science Studies (QSS), 2022, ISSN 2641-3337
Editore: MIT Press
DOI: 10.1162/qss_a_00166

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Autori: Niccolò Marini; Niccolò Marini; Sebastian Otálora; Sebastian Otálora; Damian Podareanu; Mart van Rijthoven; Jeroen van der Laak; Jeroen van der Laak; Francesco Ciompi; Henning Müller; Henning Müller; Manfredo Atzori; Manfredo Atzori
Pubblicato in: Frontiers in Computer Science, Vol 3 (2021), Numero 5, 2021, ISSN 2624-9898
Editore: Frontiers
DOI: 10.3389/fcomp.2021.684521

Data credit distribution: A new method to estimate databases impact

Autori: Dennis Dosso, Gianmaria Silvello
Pubblicato in: Journal of Informetrics, Numero 14/4, 2020, Pagina/e 101080, ISSN 1751-1577
Editore: Elsevier BV
DOI: 10.1016/j.joi.2020.101080

Elena Ranguelova, Christiaan Meijer, Leon Oostrum, Yang Liu, Patrick Bos, Giulia Crocioni, Matthieu Laneuville, Bryan Cardenas Guevara, Rena Bakhshi, and Damian Podareanu

Autori: Elena Ranguelova, Christiaan Meijer, Leon Oostrum, Yang Liu, Patrick Bos, Giulia Crocioni, Matthieu Laneuville, Bryan Cardenas Guevara, Rena Bakhshi, and Damian Podareanu
Pubblicato in: Journal of Open Source Software, 2022, ISSN 2475-9066
Editore: JOSS
DOI: 10.21105/joss.04493

TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction.

Autori: Stefano Marchesin; Gianmaria Silvello
Pubblicato in: BMC Bioinformatics, 2022, ISSN 1471-2105
Editore: BioMed Central
DOI: 10.1186/s12859-022-04646-6

State-of-the-Art Deep Learning in Cardiovascular Image Analysis

Autori: Geert Litjens, Francesco Ciompi, Jelmer M. Wolterink, Bob D. de Vos, Tim Leiner, Jonas Teuwen, Ivana Išgum
Pubblicato in: JACC: Cardiovascular Imaging, Numero 12/8, 2019, Pagina/e 1549-1565, ISSN 1936-878X
Editore: Elsevier BV
DOI: 10.1016/j.jcmg.2019.06.009

MedTAG: A Portable and Customizable Annotation Tool for Biomedical Documents

Autori: Fabio Giachelle, Ornella Irrera and Gianmaria Silvello
Pubblicato in: BMC Medical Informatics and Decision Making, 2021, ISSN 1472-6947
Editore: BioMed Central
DOI: 10.1186/s12911-021-01706-4

Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology

Autori: Sebastian Otálora, Manfredo Atzori, Vincent Andrearczyk, Amjad Khan, Henning Müller
Pubblicato in: Frontiers in Bioengineering and Biotechnology, Numero 7, 2019, ISSN 2296-4185
Editore: Frontiers
DOI: 10.3389/fbioe.2019.00198

Deep learning-based retrieval system for gigapixel histopathology cases and the open access literature

Autori: Roger Schaer, Sebastian Otálora, Oscar Jimenez-del-Toro, Manfredo Atzori, Henning Müller
Pubblicato in: Journal of Pathology Informatics, Numero 10/1, 2019, Pagina/e 19, ISSN 2153-3539
Editore: Wolters Kluwer Medknow
DOI: 10.4103/jpi.jpi_88_18

Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System

Autori: Dennis Dosso, Gianmaria Silvello
Pubblicato in: IEEE Access, Numero 8, 2020, Pagina/e 14089-14111, ISSN 2169-3536
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/ACCESS.2020.2966823

Focal elements of neural information retrieval models. An outlook through a reproducibility study

Autori: Stefano Marchesin, Alberto Purpura, Gianmaria Silvello
Pubblicato in: Information Processing & Management, 2019, Pagina/e 102109, ISSN 0306-4573
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.ipm.2019.102109

A systematic review of Automatic Term Extraction: What happened in 2022?

Autori: Giorgio Maria Di Nunzio, Stefano Marchesin, Gianmaria Silvello
Pubblicato in: Digital Scholarship in the Humanities, 2023, Pagina/e i41–i47, ISSN 2055-7671
Editore: Oxford University Press
DOI: 10.1093/llc/fqad030

Data-driven color augmentation for H&E stained images in computational pathology

Autori: Niccolò Marini, Sebastian Otalora, Marek Wodzinski, Selene Tomassini, Aldo Franco Dragoni, Stephane Marchand-Maillet, Juan Pedro Dominguez Morales, Lourdes Duran-Lopez, Simona Vatrano, Henning Müller, Manfredo Atzori
Pubblicato in: Journal of Pathology Informatics, 2023, ISSN 2153-3539
Editore: Elsevier
DOI: 10.1016/j.jpi.2022.100183

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

Autori: Mercan, C., Balkenhol, M., Salgado, R., Sherman, M., Vielh, P., Vreuls, W., Polónia, A., Horlings, H. M., Weichert, W., Carter, J. M., Bult, P., Christgen, M., Denkert, C., van der Vijven, K., Bokhorst, JM., van der Laak, J., & Ciompi, F.
Pubblicato in: npj Breast Cancer, 2022, ISSN 2374-4677
Editore: Nature
DOI: 10.1038/s41523-022-00488-w

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization

Autori: Graziani, Mara, Otalora, Sebastian, Marchand-Maillet, Stéphane., Müller, Henning, & Andrearczyk, Vincent
Pubblicato in: Journal of Machine Learning for Biomedical Imaging, 2023, ISSN 2766-905X
Editore: Melba editors
DOI: 10.21203/rs.3.rs-744740/v3

A Decade of GigaScience: The Challenges of Gigapixel Pathology Images

Autori: Geert Litjens, Francesco Ciompi, Jeroen van der Laak
Pubblicato in: GigaScience, 2022, ISSN 2047-217X
Editore: Oxford Academics
DOI: 10.1093/gigascience/giac056

Deep learning in histopathology: the path to the clinic

Autori: J. van der Laak, G. Litjens and F. Ciompi
Pubblicato in: Nature Medicine, 2021, ISSN 1546-170X
Editore: Nature
DOI: 10.1038/s41591-021-01343-4

Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment

Autori: Smit, M. A., Ciompi, F., Bokhorst, J. M., van Pelt, G. W., Geessink, O. G., Putter, H., Tollenaar, R. A., van Krieken, J. H., Mesker, W. E., & van der Laak, J. A
Pubblicato in: Journal of Pathology Informatics, 2023, ISSN 2153-3539
Editore: Elsevier
DOI: 10.1016/j.jpi.2023.100191

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images

Autori: Bokhorst, J. M., Nagtegaal, I. D., Fraggetta, F., Vatrano, S., Mesker, W., Vieth, M., van der Laak, J., & Ciompi, F
Pubblicato in: Scientific Reports, 2023, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-023-35491-z

Semi-supervised learning to automate tumor bud detection in cytokeratin-stained whole-slide images of colorectal cancer.

Autori: Bokhorst, J. M., Nagtegaal, I. D., Zlobec, I., Dawson, H., Sheahan, K., Simmer, F., Kirsch, R., Vieth, M., Lugli, A., van der Laak, J., & Ciompi, F.
Pubblicato in: Cancers, 2023, ISSN 2072-6694
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers15072079

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Autori: Mart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, Jeroen van der Laak, Francesco Ciompi
Pubblicato in: Medical Image Analysis, 2021, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2020.101890

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Autori: Niccolò Marini; Stefano Marchesin; Sebastian Otálora; Marek Wodzinski; Alessandro Caputo; Mart van Rijthoven; Witali Aswolinskiy; John-Melle Bokhorst; Damian Podareanu; Edyta Petters; Svetla Boytcheva; Genziana Buttafuoco; Simona Vatrano; Filippo Fraggetta; Jeroen van der Laak; Maristella Agosti; Francesco Ciompi; Gianmaria Silvello; Henning Muller; Manfredo Atzori
Pubblicato in: Npj Digital Medicine, Numero 5, 2022, ISSN 2398-6352
Editore: NPJ - Nature Partner Journals
DOI: 10.1038/s41746-022-00635-4

Modelling Digital Health Data: The ExaMode Ontology for Computational Pathology

Autori: Gianmaria Silvello, Laura Menotti, Manfredo Atzori, Svetla Boytcheva,Francesco Ciompi, Giorgio Maria Di Nunzio, Filippo Fraggetta, Fabio Giachelle, Ornella Irrera, Stefano Marchesin, Niccolò Marini, Henning Müller, and Todor Primov
Pubblicato in: Journal of Pathology Informatics, 2023, ISSN 2153-3539
Editore: Elsevier
DOI: 10.1016/j.jpi.2023.100332

Report on the 2nd international conference on design of experimental search & information retrieval systems (DESIRES 2021)

Autori: Alonso, O., Marchesin, S., Najork, M., & Silvello, G... (Vol. 55, No. 2, pp. 1-13).
Pubblicato in: In ACM SIGIR Forum, 2022, ISSN 0163-5840
Editore: ACM

An Information Visualization Tool for the Interactive Component-Based Evaluation of Search Engines

Autori: Giacomo Rocco, Gianmaria Silvello
Pubblicato in: Digital Libraries: The Era of Big Data and Data Science - 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings, Numero 1177, 2020, Pagina/e 15-25, ISBN 978-3-030-39904-7
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-39905-4_3

Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Autori: Henning Müller, Vincent Andrearczyk, Oscar Jimenez del Toro, Anjani Dhrangadhariya, Roger Schaer, Manfredo Atzori
Pubblicato in: MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II, Numero 11962, 2020, Pagina/e 553-564, ISBN 978-3-030-37733-5
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-37734-2_45

A Framework for Citing Nanopublications

Autori: Erika Fabris, Tobias Kuhn, Gianmaria Silvello
Pubblicato in: Digital Libraries for Open Knowledge - 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Oslo, Norway, September 9-12, 2019, Proceedings, Numero 11799, 2019, Pagina/e 70-83, ISBN 978-3-030-30759-2
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30760-8_6

Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features

Autori: Mara Graziani, Vincent Andrearczyk, Henning Müller
Pubblicato in: State of the Art in Neural Networks and Their Applications, 2023
Editore: Academic Press
DOI: 10.1016/b978-0-12-819872-8.00015-x

Terminologie numérique : conception, représentation et gestion

Autori: Vezzani, Federica
Pubblicato in: LINGUISTIC INSIGHTS, Numero 2, 2021, ISBN 9783034342643
Editore: Peter Lang
DOI: 10.3726/b19407

Developing Unsupervised Knowledge-Enhanced Models to Reduce the Semantic Gap in Information Retrieval

Autori: S. Marchesin
Pubblicato in: 2020
Editore: UNIPD
DOI: 10.1145/3476415.3476433

Bridging Information Access and Visual Analytics Methods for Supporting the Decision Process in the Digital Pathology Domain

Autori: Fabio Giachelle
Pubblicato in: 2022
Editore: UNIPD

È in corso la ricerca di dati su OpenAIRE...

Risultati finali

Pubblicazioni

Condividi questa pagina

Scarica