Multimorbidity Content-Based Medical Image Retrieval and Disease Recognition Using Multi-Label Proxy Metric Learning (with Yunyan Xing, Ben Meyer, Mehrtash Harandi and Zongyuan Ge)

This paper shows how to do content-based retrieval on medical images where each image can have many simultaneous diagnoses (multimorbidity), by contrast to standard metric learning methods that assume a single class per sample. A multi-label proxy-based metric learning framework learns a single embedding in which images sharing any subset of labels are pulled together in a structured way, supporting both retrieval of similar cases and direct multi-label disease recognition on the same embedding.

[IEEE Access 2023 paper]

Deep Laparoscopic Stereo Matching with Transformers (with Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Zhiyong Wang and Zongyuan Ge)

This paper shows how to do stereo matching in the particularly difficult domain of laparoscopic surgery, where the classical stereo assumptions break down: tissue is textureless or specular, illumination changes rapidly with camera motion, and the scene contains thin instruments with sharp depth discontinuities. A transformer-based matching module is used to aggregate context across the whole image, combined with a new laparoscopic stereo dataset, producing noticeably better depth estimates than CNN-based stereo networks designed for driving scenes.

[MICCAI 2022 paper]