There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

"Too Long; Didn't Read": a short sentence describing your paper

Abstract of paper. Add TeX formulas using the following formats: $In-line Formula$ or $$Block Formula$$

Search author profile by first, middle and last name or email address. If the profile is not found, you can add the author by completing first, middle, and last names as well as author email address.

Similarly, please either provide links to the datasets used (comma-separated if necessary) or an explanation why they cannot be published.

In addition to original work, we also specifically accept short papers discussing recently published or submitted journal contributions to give authors the opportunity to present their work and obtain feedback from conference attendees that they would not otherwise receive. Double submissions to other conferences are NOT acceptable. Please select the status of your paper below.

What best describes the contribution of your paper?

Upload a PDF file that ends with .pdf. Please carefully read the author instructions at https://2021.midl.io/author-instructions.html and in the call for papers at https://2021.midl.io/call-for-papers.html. Short papers are subject to a page limit of 3 PAGES TOTAL including references, with no additional allowance for appendix nor supplementary material. PDFs exceeding 3 pages or not using the official style will be desk-rejected. Submissions are single-blind: please include full author names and affiliations in the paper.

MIDL highly encourages open access for the data and code, and reviewers are instructed to take availability of source code and data as well as reproducibility into account. We therefore highly recommend to publish the source code together with the paper and provide the URL in the field below. If you feel that you cannot publish the source code at this point or this does not apply to your submission (e.g., your paper is theory-only), please provide an explanation of the specific reasons instead. 

Title of paper. Add TeX formulas using the following formats: $In-line Formula$ or $$Block Formula$$

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

Brennan Nichyporuk, Justin Szeto, Douglas Arnold, Tal Arbel