MICCAI 2021 Daily – Wednesday

The multiple instance learning technique used in this work comes from digital pathology , where you have very large whole-slide images that are too big for the computer to deal with, so you crop them into smaller tiles and learn the features. Then at the end you aggregate the information from all tiles to get an understanding of the whole-slide image. This perfectly fits the problem here, which is to predict a patient’s survival based on a number of tumors. Jianan tells us one of the biggest challenges has been collecting data . “ When we look at survival of patients, we collect patient data that has had some kind of treatment, ” he tells us. “ You might be surprised, but 80 per cent of colorectal cancer liver metastasis patients, which I studied for my paper, can’t receive liver resection and that is the only curative treatment for them. That’s why we don’t have a lot of data on them. Existing datasets include mostly unifocal patients because we know how to treat them, but it’s difficult to collect a large database with multifocal cancer patients. We don’t know how multiple tumors affect patient survival or how aggressive a tumor is. ” 9 DAILY MICCAI Wednesday Jianan Chen Example MRI scan of a CRLM patient who had multifocal cancer. Existing biomarkers only use information from the largest lesion (marked in red) while ignoring information from other lesions (marked in orange).