تقرير
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
| العنوان: | Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images |
|---|---|
| المؤلفون: | Thandiackal, Kevin, Chen, Boqi, Pati, Pushpak, Jaume, Guillaume, Williamson, Drew F. K., Gabrani, Maria, Goksel, Orcun |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requirements, and constrains the contextualization of the WSI-level representation to a single scale. A few MIL methods extend to multiple scales, but are computationally more demanding. In this paper, inspired by the pathological diagnostic process, we propose ZoomMIL, a method that learns to perform multi-level zooming in an end-to-end manner. ZoomMIL builds WSI representations by aggregating tissue-context information from multiple magnifications. The proposed method outperforms the state-of-the-art MIL methods in WSI classification on two large datasets, while significantly reducing the computational demands with regard to Floating-Point Operations (FLOPs) and processing time by up to 40x. Comment: Typos corrected; Changed dataset name from INSEC to CRC upon dataset creators' request; Update affiliation and fix typos |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2204.12454 |
| رقم الأكسشن: | edsarx.2204.12454 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |