Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

• URL: http://arxiv.org/abs/2011.08939v3
• Date: Fri, 2 Apr 2021 16:48:03 GMT
• Title: Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
• Authors: Bin Li, Yin Li, Kevin W. Eliceiri
• Abstract summary: We address the challenging problem of whole slide image (WSI) classification. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations.
• Score: 16.84711797934138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems. GitHub repository: https://github.com/binli123/dsmil-wsi

Related papers

• Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification [51.95824566163554]
  We argue that synergizing the standard MIL assumption with variational inference encourages the model to focus on tumour morphology instead of spurious correlations. Our method also achieves better classification boundaries for identifying hard instances and mitigates the effect of spurious correlations between bags and labels.
  arXiv  Detail & Related papers  (2024-08-18T12:15:22Z)
• MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models [56.37780601189795]
  We propose a framework named MamMIL for WSI analysis. We represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we propose a topology-aware scanning mechanism.
  arXiv  Detail & Related papers  (2024-03-08T09:02:13Z)
• Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification [3.2721526745176144]
  We introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches. We evaluate our method on two public WSI datasets including Camelyon-16 and TCGA lung cancer.
  arXiv  Detail & Related papers  (2024-02-16T11:28:50Z)
• Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests [59.623267208433255]
  Multiple Instance Learning (MIL) is a sub-domain of classification problems with positive and negative labels and a "bag" of inputs. In this work, we examine five of the most prominent deep-MIL models and find that none of them respects the standard MIL assumption. We identify and demonstrate this problem via a proposed "algorithmic unit test", where we create synthetic datasets that can be solved by a MIL respecting model.
  arXiv  Detail & Related papers  (2023-10-27T03:05:11Z)
• TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification [13.195971707693365]
  We develop a Trainable Prototype enhanced deep MIL framework for weakly supervised WSI classification. Our method is able to reveal the correlations between different tumor subtypes through distances between corresponding trained prototypes. We test our method on two WSI datasets and it achieves a new SOTA.
  arXiv  Detail & Related papers  (2023-05-01T07:39:19Z)
• Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection [74.80595632328094]
  Multiple Instance Learning (MIL) is prevailing in Weakly Supervised Video Anomaly Detection (WSVAD) We propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD.
  arXiv  Detail & Related papers  (2023-03-22T08:11:22Z)
• ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification [14.78430890440035]
  Whole slide image (WSI) classification often relies on weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels. We propose ReMix, a general and efficient framework for MIL based WSI classification.
  arXiv  Detail & Related papers  (2022-07-05T04:21:35Z)
• Feature Re-calibration based MIL for Whole Slide Image Classification [7.92885032436243]
  Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases. We propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder.
  arXiv  Detail & Related papers  (2022-06-22T07:00:39Z)
• Improving Calibration for Long-Tailed Recognition [68.32848696795519]
  We propose two methods to improve calibration and performance in such scenarios. For dataset bias due to different samplers, we propose shifted batch normalization. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-01T13:55:21Z)
• Dual-stream Maximum Self-attention Multi-instance Learning [11.685285490589981]
  Multi-instance learning (MIL) is a form of weakly supervised learning where a single class label is assigned to a bag of instances while the instance-level labels are not available. We propose a dual-stream maximum self-attention MIL model (DSMIL) parameterized by neural networks. Our method achieves superior performance compared to the best MIL methods and demonstrates state-of-the-art performance on benchmark MIL datasets.
  arXiv  Detail & Related papers  (2020-06-09T22:44:58Z)
• Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning [82.41415008107502]
  Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments) We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions.
  arXiv  Detail & Related papers  (2020-03-31T23:36:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.