Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search

TL;DR

This paper introduces a novel deep generative framework for learning binary and sparse permutation-invariant representations of whole slide images, significantly improving retrieval speed and memory efficiency while maintaining high accuracy.

Contribution

It proposes new loss functions and a deep generative approach for binary and sparse WSI representations, addressing memory and efficiency issues in current methods.

Findings

Outperforms Yottixel in retrieval accuracy and speed

Achieves competitive WSI classification performance on LKS dataset

Validates effectiveness on TCGA and LKS datasets

Abstract

Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean space not ideal for efficient WSI retrieval. Furthermore, most of the current methods require high GPU memory due to the simultaneous processing of multiple sets of patches. To address these challenges, we propose a novel framework for learning binary and sparse WSI representations utilizing a deep generative modelling and the Fisher Vector. We introduce new loss functions for learning sparse and binary permutation-invariant WSI representations that employ instance-based training achieving better memory efficiency. The learned WSI representations are validated on The Cancer Genomic Atlas (TCGA) and Liver-Kidney-Stomach (LKS) datasets. The proposed method outperforms Yottixel (a recent search engine for histopathology images) both in terms of retrieval accuracy and speed. Further, we achieve competitive performance against SOTA on the public benchmark LKS dataset for WSI classification.