Can I read Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images on EtoBox?

Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images by Raza, Manahil; Awan, Ruqayya; Bashir, Raja Muhammad Saad; Qaiser, Talha; Rajpoot, Nasir M. is a scholarly article available to read on EtoBox.

What is Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images about?

Digital whole slide images (WSIs) are generally captured at microscopic resolution and encompass extensive spatial data. Directly feeding these images to deep learning models is computationally intractable due to memory constraints, while downsampling the WSIs risks incurring information loss. Alternatively, splitting the WSIs into smaller patches may result in a loss of important contextual information. In this paper, we propose a novel dual attention approach, consisting of two main components, both inspired by the visual examination process of a pathologist: The first soft attention model processes a low magnification view of the WSI to identify relevant regions of interest, followed by a custom sampling method to extract diverse and spatially distinct image tiles from the selected ROIs. The second component, the hard attention classification model further extracts a sequence of multi-resolution glimpses from each tile for classification. Since hard attention is non-differentiable, we train this component using reinforcement learning to predict the location of the glimpses. This approach allows the model to focus on essential regions instead of processing the entire tile, thereb

Author

Raza, Manahil; Awan, Ruqayya; Bashir, Raja Muhammad Saad; Qaiser, Talha; Rajpoot, Nasir M.

Published

2023

Language

EN