mlsanityData to 14 Oct 2026

Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model

arXiv 2505.21564 ↗First posted 27 May 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

In deep multi-instance learning, the number of applicable instances depends on the data set. In histopathology images, deep learning multi-instance learners usually assume there are hundreds to thousands instances in a bag. However, when the number of instances in a bag increases to 256 in brain hematoma CT, learning becomes extremely difficult. In this paper, we address this drawback. To overcome this problem, we propose using a pre-trained model with self-supervised learning for the multi-instance learner as a downstream task. With this method, even when the original target task suffers from the spurious correlation problem, we show improvements of 5% to 13% in accuracy and 40% to 55% in the F1 measure for the hypodensity marker classification of brain hematoma CT.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 27 May 2025.

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References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  4. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  6. 2016Language Modeling with Gated Convolutional Networkscited by 702
  7. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  8. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  9. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  10. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 60
  11. 2021A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learningcited by 19

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