mlsanityData to 7 Oct 2026

Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images

arXiv 2403.14346 ↗First posted 21 Mar 2024cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data. However, current methods fall short in bridging the domain gap between pretrained models and medical imaging, and often fail to account for spatial relationships across different magnifications. Addressing these challenges, we introduce the Concentric Dual Fusion Attention-MIL (CDFA-MIL) framework,which innovatively combines point-to-area feature-colum attention and point-to-point concentric-row attention using concentric patch. This approach is designed to effectively fuse correlated information, enhancing feature representation and providing stronger correlation guidance for WSI analysis. CDFA-MIL distinguishes itself by offering a robust fusion strategy that leads to superior WSI recognition. Its application has demonstrated exceptional performance, significantly surpassing existing MIL methods in accuracy and F1 scores on prominent datasets like Camelyon16 and TCGA-NSCLC. Specifically, CDFA-MIL achieved an average accuracy and F1-score of 93.7% and 94.1% respectively on these datasets, marking a notable advancement over traditional MIL approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

26 of 46 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  5. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  6. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  7. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  8. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  9. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  10. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  11. 2019Deep neural network models for computational histopathology: A surveycited by 165
  12. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
14 more
  1. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  2. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  3. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
  4. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  5. 2020HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide imagescited by 43
  6. 2020Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histologycited by 43
  7. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  8. 2019Streaming convolutional neural networks for end-to-end learning with multi-megapixel imagescited by 40
  9. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  10. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  11. 2019Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecturecited by 19
  12. 2021PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learningcited by 6
  13. 2022Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch Embeddingscited by 2
  14. 2022Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Imagescited by 2

Built on by

Not cited yet.