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

Keywords: interpretebility, label-efficient learning, self-supervised, landmark detection, clinical knowledge, contrast phase, CT

TL;DR: We use self-supervised embeddings to detect contrast-related anatomical landmarks in CT, and then use clinical prior knowledge to classify the contrast phase.

Abstract: 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.

Paper Type: both

Primary Subject Area: Application: Radiology

Secondary Subject Area: Interpretability and Explainable AI

Paper Status: original work, not submitted yet

Source Code Url: Source code releasing is not approved at this point due to company policy.

Data Set Url: Dataset is in-house.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/interpretable-medical-image-classification/code)