Asset Details
Do you wish to reserve the book?
Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases
Do you wish to request the book?
Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases
2025
Overview
Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a widely used approach for weakly-supervised tumor detection with large-scale data without the need for manual annotations. However, traditional MIL methods often depend on classification tasks that require tumor-free cases as negative examples, which are challenging to obtain in real-world clinical workflows, especially for surgical resection specimens. We address this limitation by reformulating tumor detection as a
regression
task, estimating tumor percentages from WSIs, a clinically available target across multiple cancer types. In this paper, we provide an analysis of the proposed weakly-supervised regression framework by applying it to multiple organs, specimen types and clinical scenarios. We characterize the robustness of our framework to tumor percentage as a noisy regression target, and introduce a novel concept of
“amplification technique”
to improve tumor detection sensitivity when learning from small tumor regions. Finally, we provide interpretable insights into the model’s predictions by analyzing visual attention and logit maps. Our code is available at
https://github.com/imi-bigpicture/tumor-percentage-mil-regression
.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
