G5 - Interpretable Medical Image Classification with Self-Supervised Anatomical Embedding and Prior Knowledge
Ke Yan, Youbao Tang, Adam P Harrison, Jinzheng Cai, Le Lu, Jingjing Lu
In medical image analysis tasks, it is important to make machine learning models focus on correct anatomical locations, so as to improve interpretability and robustness of the model. We adopt a latest algorithm called self-supervised anatomical embedding (SAM) to locate point of interest (POI) on computed tomography (CT) scans. SAM can detect arbitrary POI with only one labeled sample needed. Then, we can extract targeted features from the POIs to train a simple prediction model guided by clinical prior knowledge. This approach mimics the practice of human radiologists, thus is interpretable, controllable, and robust. We illustrate our approach on the application of CT contrast phase classification and it outperforms an existing deep learning based method trained on the whole image.
Thursday 8th July
G1-9 (short): Interpretability and Explainable AI - 16:45 - 17:30 (UTC+2)