NeurIPS 2025

Sequential Attention-based Sampling for
Histopathological Analysis
Tarun G
Indian Institute of Science, Bangalore, India
tarung@iisc.ac.in
Naman Malpani
Indian Institute of Science, Bangalore, India
namanmalpani@iisc.ac.in
Gugan Thoppe
Indian Institute of Science, Bangalore, India
gthoppe@iisc.ac.in
Devarajan Sridharan
Indian Institute of Science, Bangalore, India
sridhar@iisc.ac.in
Abstract
Deep neural networks are increasingly applied in automated histopathology. Yet,
whole-slide images (WSIs) are often acquired at gigapixel sizes, rendering them
computationally infeasible to analyze entirely at high resolution. Diagnostic labels
are largely available only at the slide-level, because expert annotation of images
at a finer (patch) level is both laborious and expensive. Moreover, regions with
diagnostic information typically occupy only a small fraction of the WSI, mak-
ing it inefficient to examine the entire slide at full resolution. Here, we propose
SASHA – Sequential Attention-based Sampling for Histopathological Analysis – a
deep reinforcement learning approach for efficient analysis of histopathological
images. First, SASHA learns informative features with a lightweight hierarchical,
attention-based multiple instance learning (MIL) model. Second, SASHA samples
intelligently and zooms selectively into a small fraction (10-20%) of high-resolution
patches to achieve reliable diagnoses. We show that SASHA matches state-of-the-
art methods that analyze the WSI fully at high resolution, albeit at a fraction of
their computational and memory costs. In addition, it significantly outperforms
competing, sparse sampling methods. We propose SASHA as an intelligent sam-
pling model for medical imaging challenges that involve automated diagnosis with
exceptionally large images containing sparsely informative features1.
1
Introduction
Deep learning models have emerged as a major frontier in automated medical imaging diagnosis. For
example, such models accurately identify tum...