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Deep Learning Based Interpretable Pediatric Brain Tumors Segmentation and Classification

Project description

AI-based framework to classify paediatric brain tumours

Paediatric brain tumours (PBTs) are the leading cause of cancer-related deaths in children and adolescents. AI technologies aid doctors in the detection and diagnosis of PBTs through clinical decision support systems (CDSS). However, due to limited medical image datasets, doctors require assistance in segmenting PBTs. Moreover, concerns about the lack of transparency in black-box AI models hinder the adoption of AI in CDSS among doctors. With the support of the Marie Skłodowska-Curie Actions programme, the DL-I-PBraTSC project aims to develop an advanced AI-based framework for classifying primary brain tumours (PBTs) in children and adolescents. This helps in diagnosing, planning treatment, and predicting patient outcomes. The project will gather large, balanced PBT medical images from a secondary hospital and use an online test platform to collect feedback.

Objective

The DL-I-PBraTSC project aims to address the significant impact of pediatric brain tumors (PBTs) as the leading cause of cancer death in children and adolescents. Artificial Intelligence (AI) technologies are increasingly being explored to assist doctors in detecting and diagnosing through clinical decision support systems (CDSS). However, They face the challenges in successfully segmenting PBTs due to the scarcity of available medical image datasets. Additionally, the lack of transparency in black-box AI models has raised concerns among doctors, hindering the adoption of AI in CDSS. To tackle these challenges, the project will develop a state-of-the-art interpretable AI-based framework to classify PBTs including tumor segmentation. DL-I-PBraTSC will identify the location of PBTs, classify of PBT types, and enable quantitative analysis of sub-region of PBT parameters helping clinicians in diagnosis, treatment planning, monitoring disease progression, and predicting patient outcomes. The project will start with collecting and preparing sufficiently large, balanced PBT medical images from secondment institution with the assistance of medical experts. An online test platform will be implemented for clinicians to use the model, gathering feedback for further validation and improvements. The non-academic placement will provide real-world clinical validation of the model's efficacy. The project findings will be shared in conferences or journals targeting both neuroscience and informatics. DL-I-PBraTSC can help healthcare providers make more informed decisions about diagnosis and treatment planning of PBTs and contribute to early detection and intervention. These can lead to better patient outcomes, improved overall healthcare delivery and public health outcomes, and reduced healthcare costs, aligning with the EU's objectives of providing ensuring the safety and well-being of its citizens and one of the Irish national research priorities areas, Health and Wellbeing.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSITY OF GALWAY
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 269 418,00
Address
UNIVERSITY ROAD
H91 Galway
Ireland

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Region
Ireland Northern and Western West
Activity type
Higher or Secondary Education Establishments
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Total cost

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