mlsanityData to 7 Oct 2026

Gigapixel Whole-Slide Images Classification using Locally Supervised Learning

arXiv 2207.08267 ↗First posted 17 Jul 2022cs.CVcited by 15

Not on the map. Why?

Abstract (arXiv)

Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tools for processing WSIs is challenging due to their enormous sizes. Currently, to deal with this issue, conventional methods rely on a multiple instance learning (MIL) strategy to process a WSI at patch level. Although effective, such methods are computationally expensive, because tiling a WSI into patches takes time and does not explore the spatial relations between these tiles. To tackle these limitations, we propose a locally supervised learning framework which processes the entire slide by exploring the entire local and global information that it contains. This framework divides a pre-trained network into several modules and optimizes each module locally using an auxiliary model. We also introduce a random feature reconstruction unit (RFR) to preserve distinguishing features during training and improve the performance of our method by 1% to 3%. Extensive experiments on three publicly available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority of our method on different classification tasks. Our method outperforms the state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times faster. Additionally, when dividing it into eight modules, our method requires as little as 20% of the total gpu memory required by end-to-end training. Our code is available at https://github.com/cvlab-stonybrook/local_learning_wsi.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 17 Jul 2022.

Builds on

18 of 26 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  4. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  5. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  6. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  7. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  8. 2019Training Neural Networks with Local Error Signalscited by 135
  9. 2018Greedy Layerwise Learning Can Scale to ImageNetcited by 113
  10. 2019Decoupled Greedy Learning of CNNscited by 76
  11. 2021Revisiting Locally Supervised Learning: an Alternative to End-to-end Trainingcited by 71
  12. 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
6 more
  1. 2019Deep Learning for Whole Slide Image Analysis: An Overviewcited by 51
  2. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
  3. 2019Streaming convolutional neural networks for end-to-end learning with multi-megapixel imagescited by 40
  4. 2019Multi-Stage Pathological Image Classification using Semantic Segmentationcited by 13
  5. 2019Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancercited by 12
  6. 2020SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classificationcited by 12

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 9; 2024: 4; 2025: 1.

15 papers.

Sort:

  1. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  2. 2023Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuningcited by 15
  3. 2024AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classificationcited by 10
  4. 2023TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classificationcited by 5
  5. 2022Local Learning on Transformers via Feature Reconstructioncited by 4
  6. 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
  7. 2023Module-wise Training of Neural Networks via the Minimizing Movement Schemecited by 4
  8. 2024Dataset Distillation for Histopathology Image Classificationcited by 4
  9. 2023Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classificationcited by 3
  10. 2024Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysiscited by 3
  11. 2024Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohortcited by 1
  12. 2023Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Trainingcited by 0
3 more
  1. 2023Exploring shared memory architectures for end-to-end gigapixel deep learningcited by 0
  2. 2023Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supplycited by 0
  3. 2025MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learningcited by 0