Project description
Breaking the ‘black box’ of AI pathology
Pathology is the definitive stage of cancer diagnosis. However, there is a global shortage of pathologists and a surge in the complexity of tissue data. While AI foundation models offer a powerful way to process gigapixel images, they often function as ‘black boxes’. An AI that provides a ‘cancer’ or ‘not cancer’ label without explaining its reasoning is of limited utility. The ERC-funded DeepSPIM project aims to integrate large language models (LLMs) with automated tissue segmentation to create an interface where pathologists can review a slide. Instead of accepting a passive result, a doctor can ask the AI to quantify specific biomarkers or explain which cellular features led to a particular risk score, thereby supporting more accurate diagnoses.
Objective
Progress in deep learning brings new tools and methods that start reshaping pathology practice and advancing research in oncology. As a postdoctoral researcher in the Mahmood Lab (Harvard Medical School, 2022–), I played a pivotal role in the development and evaluation of “foundation models” for pathology – general-purpose models that can be used for various downstream tasks. Despite advances, foundation models still face several key limitations that restrict their widespread adoption. First, these models are designed to provide “non-human interpretable” data representation, providing little actionable insights to practitioners. Moreover, they lack robust language interfacing capabilities, especially in providing quantitative information. Finally, they are predominantly focused on hematoxylin & eosin (H&E) staining, leaving other histopathology modalities largely untouched. In this context, I hypothesize that foundation models for pathology will remain limited in impact unless they incorporate key additional features: (a) off-the-shelf structure segmentation and phenotyping for downstream analysis, (b) interactive language interfacing capable of answering quantitative queries, and (c) expanded support for other modalities, e.g. immunohistochemistry. To bring these ideas to life, I introduce DeepSPIM. In DeepSPIM, I will semi-automate data labeling to train segmentation models that will be combined with large language modeling to build a language interface. I will evaluate the translational capabilities of the models in assisting pathologists with routine clinical tasks, and, exploring interpretable morphological correlates of treatment response in oncology. The goals of DeepSPIM are closely aligned with the technical skills I developed throughout my PhD and postdoc. Leveraging my mentoring experience, professional network, and support of the University of Lausanne and the ERC Program, I aim to establish an independent research group in computational pathology.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences clinical medicine oncology
- medical and health sciences basic medicine pathology
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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HORIZON.1.1 - European Research Council (ERC)
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Call for proposal
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(opens in new window) ERC-2025-STG
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1015 LAUSANNE
Switzerland
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