mlsanityData to 14 Oct 2026

SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification

arXiv 2509.03973 ↗First posted 4 Sep 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position information and a SAC block to perform full instance correlations. The positional encoding module utilizes the instance coordinates within the slide to encode the spatial relationships instead of the instance index in the input WSI sequence. The positional encoding module can also handle the length extrapolation issue where the training and testing sequences have different lengths. The SAC block is an MLP-based method that performs full instance correlation in linear time complexity with respect to the sequence length. Due to the simple structure of MLP, it is easy to deploy since it does not require custom CUDA kernels, compared to Transformer-based methods for WSI classification. SAC-MIL has achieved state-of-the-art performance on the CAMELYON-16, TCGA-LUNG, and TCGA-BRAC datasets. The code will be released upon acceptance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 55 references that are in the map. First posted on arXiv 4 Sep 2025.

Builds on

46 of 55 references.

References · 46

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  6. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  7. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  8. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  9. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
  10. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  11. 2017Deformable Convolutional Networkscited by 1,488
  12. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
34 more
  1. 2018Self-Attention with Relative Position Representationscited by 1,120
  2. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  4. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  5. 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 740
  6. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  7. 2021MetaFormer Is Actually What You Need for Visioncited by 524
  8. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  9. 2021Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attentioncited by 471
  10. 2021Conditional Positional Encodings for Vision Transformerscited by 406
  11. 2021Pay Attention to MLPscited by 368
  12. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  13. 2021ResMLP: Feedforward networks for image classification with data-efficient trainingcited by 339
  14. 2023Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Actioncited by 302
  15. 2021Global Filter Networks for Image Classificationcited by 260
  16. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  17. 2024Rotary Position Embedding for Vision Transformercited by 236
  18. 2023LongNet: Scaling Transformers to 1,000,000,000 Tokenscited by 204
  19. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  20. 2022A Length-Extrapolatable Transformercited by 127
  21. 2021CycleMLP: A MLP-like Architecture for Dense Predictioncited by 126
  22. 2021Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognitioncited by 103
  23. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 88
  24. 2024FiT: Flexible Vision Transformer for Diffusion Modelcited by 85
  25. 2022A graph-transformer for whole slide image classificationcited by 84
  26. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 67
  27. 2021An Image Patch is a Wave: Phase-Aware Vision MLPcited by 61
  28. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 60
  29. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
  30. 2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
  31. 2023Structured State Space Models for Multiple Instance Learning in Digital Pathologycited by 41
  32. 2023Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classificationcited by 12
  33. 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysiscited by 12
  34. 2022ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengthscited by 9

Built on by

Not cited yet.