mlsanityData to 14 Oct 2026

SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification

arXiv 2105.02726 ↗First posted 6 May 2021cs.CV cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracted from a whole slide image, which could provide valuable cues for classification. This paper presents a novel MIL approach that exploits the spatial relationship of tiles for classifying whole slide images. To do so, a sparse map is built from tiles embeddings, and is then classified by a sparse-input CNN. It obtained state-of-the-art performance over popular MIL approaches on the classification of cancer subtype involving 10000 whole slide images. Our results suggest that the proposed approach might (i) improve the representation learning of instances and (ii) exploit the context of instance embeddings to enhance the classification performance. The code of this work is open-source at {github censored for review}.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

15 of 33 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  4. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  6. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  7. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  8. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  9. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  10. 2017Submanifold Sparse Convolutional Networkscited by 223
  11. 2015Sparse 3D convolutional neural networkscited by 72
  12. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
3 more
  1. 2019Streaming convolutional neural networks for end-to-end learning with multi-megapixel imagescited by 41
  2. 2019Multiple instance learning with graph neural networkscited by 25
  3. 2017Multiple Instance Learning Networks for Fine-Grained Sentiment Analysiscited by 23

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 2.

10 papers.

  1. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  2. 2022Giga-SSL: Self-Supervised Learning for Gigapixel Imagescited by 24
  3. 2022RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessmentcited by 8
  4. 2024RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classificationcited by 7
  5. 2022Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Imagescited by 6
  6. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  7. 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
  8. 2023Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Imagescited by 3
  9. 2024Multi-Cohort Framework with Cohort-Aware Attention and Adversarial Mutual-Information Minimization for Whole Slide Image Classificationcited by 1
  10. 2025Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classificationcited by 0