| KEYWORD |
Area Engineering
Injecting prior knowledge in medical image interpretation
keywords ARTIFICIAL INTELLIGENCE, DEEP LEARNING, COMPUTER V, COMPUTER AIDED DIAGNOSIS, DEEP LEARNING, MEDICAL IMAGING
Reference persons LIA MORRA
Research Groups DAUIN - GR-09 - GRAphics and INtelligent Systems - GRAINS
Description Deep neural networks have shown remarkable performance in the interpretation of medical images. However, they require large datasets for training, and performance may degrade when training on imbalanced datasets. Radiologists are typically trained through examples, as well as through structured taxonomies that present prototypical examples of visual features associated with benign and malignant lesions. The goal of the thesis is to design neuro-symbolic architectures, such as Logic Tensor Networks, that can incorporate such constraints during training. Experiments are foreseen on mammography and possibly other medical imaging modalities.
Thesis activities include design and comparison of different neuro-symbolic techniques, starting from patch-level classification and moving on towards image-level analysis. Issues to be tackled include how to align visual features computed by the network to human-interpretable concepts and how to encode the constraints in the loss. Evaluation will be performed in terms of performance, robustness, explainability and alignment with human asssessment.
Prerequisites: programming skills (Python, Pytorch or other deep learning framework); good analytical and mathematical skills. Prior knowledge of neuro-symbolic techniques is not required – essential material to study on the topic will be provided. The candidate we are looking for is highly motivated and interested towards research-oriented activities.
Deadline 28/02/2025
PROPONI LA TUA CANDIDATURA