Ricerca CERCA

Area Engineering

Deep learning techniques for digital mammography analysis

Reference persons FABRIZIO LAMBERTI

External reference persons Lia Morra

Research Groups GR-09 - GRAphics and INtelligent Systems - GRAINS


Description Multiple thesis are available within the AIBIBANK (Bio-Banking for Artificial Intelligence) project. Research activities at DAUIN, developed in collaboration with HealthTriage srl and the DISAT department at Politecnico di Torino, aim at developing deep learning-based components for the automated screening of digital mammography images. A limited number of APPRENTICESHIP POSITIONS (Apprendistato di Alta Formazione), with compensation, are available. In early cancer prevention programs, women undergo biennal exams in which up to four, high-resolution images are acquired. The goal of the envisioned system is to triage women to identify cases with high probability of being normal, for expedite reading, and secondarily identify lesions. The problem is technically and scientifically challenging since mammography images are much larger than traditional RGB images, and subtle information from multiple views must be integrated in a single prediction; the trained networks must operate in a robust and interpretable ways. Within the project, different deep learning architectures (based on convolutional networks, transformers and relational networks) will be compared. Each thesis will focus on one or more of the following problems: i) design and comparison of multi-stream architectures can process multiple images at once, at different resolutions/levels of detail, ii) methods to estimate the uncertainty of trained neural networks, iii) data augmentation, data harmonization and domain generalization techniques to facilitate robust training across different vendors, and iv) strategies to enhance the interpretability of the trained networks and to incorporate clinical knowledge during training or in the post-hoc explanation phase. Students will be involved in a multi-disciplinary research team. Strong programming and analytical skills are required, as well as a solid background in machine learning/deep learning. Previous experience with medical imaging is a plus, but not required.

Suggested readings:
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

See also  http://grains.polito.it/work.php

Deadline 09/02/2023      PROPONI LA TUA CANDIDATURA

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