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Snuffy: Efficient Whole Slide Image Classifier

Hossein Jafarinia, Alireza Alipanah, Danial Hamdi, Saeed Razavi, Nahal Mirzaie, Mohammad Hossein Rohban

TL;DR

Snuffy architecture is introduced, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option.

Abstract

Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy.

Snuffy: Efficient Whole Slide Image Classifier

TL;DR

Snuffy architecture is introduced, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option.

Abstract

Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy.
Paper Structure (33 sections, 10 theorems, 22 equations, 11 figures, 4 tables)

This paper contains 33 sections, 10 theorems, 22 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Parameter-Efficient Fine-Tuning for Vision
  4. MIL for WSI Classification
  5. Background
  6. Notation
  7. MIL Formulation
  8. Sparse Transformers
  9. Method
  10. Continual Few-shot Efficient Self-Supervised Pre-training
  11. Snuffy Architecture
  12. Max-pooling Component
  13. Attention-pooling Component
  14. Universal Approximation of Snuffy
  15. Experiments and Results
  16. ...and 18 more sections

Key Result

theorem thmcountertheorem

A sparse transformer with any set of sparsity patterns $\{\mathcal{A}_k^l\}$ satisfying these conditions: coupled with a probability map generating a column stochastic matrix that closely approximating hardmax operator, is a universal approximator of sequence-to-sequence functions onconc.

Figures (11)

  • Figure 1: Graphical representation of the Snuffy sparsity patterns graph $G_S$ up to layer $l$. $\Lambda$ represents the set of patches that have been observed, while $N'$ denotes the set of patches that have not been covered. Please note that self-loops for all nodes are omitted for simplicity.
  • Figure 2: Overview of the proposed method (a) The WSIs are segmented into $256 \times 256$ patches at 20X magnification, followed by embedding extraction via a pre-trained ViT vit. Subsequently, these embeddings are inputted into the Snuffy for patch and WSI classification. (b) The connectivity matrix illustrates the Snuffy attention sparsity patterns, with Class-related Global Attentions, highlighted in darker colors either vertical or horizontal (the darker the more important), Diagonal Attentions depicted with pink, and Random Global Attentions shown in the lightest pink.
  • Figure 2: Ablation on number of Class-Related Global Attentions.
  • Figure 3: Qualitative view of ROIs recognized by Snuffy through its Patch Classification. (a) An example WSI from the test set of the CAMELYON16 dataset camelyon16. (b) ROIs are identified by Snuffy with black lines delineating the ground truth ROIs.
  • Figure 4: Ablation on depth.
  • ...and 6 more figures

Theorems & Definitions (18)

  • theorem thmcountertheorem
  • definition thmcounterdefinition
  • proposition thmcounterproposition
  • proof
  • lemma thmcounterlemma
  • proof
  • theorem thmcountertheorem
  • proof
  • proposition thmcounterproposition
  • proof
  • ...and 8 more