SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

• URL: http://arxiv.org/abs/2311.00048v2
• Date: Thu, 1 Aug 2024 15:37:52 GMT
• Title: SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
• Authors: Peijie Qiu, Pan Xiao, Wenhui Zhu, Yalin Wang, Aristeidis Sotiras,
• Abstract summary: Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. We propose a sparse coding MIL (SC-MIL) method that addresses the two aspects at the same time by leveraging sparse dictionary learning. The proposed SC module can be incorporated into any existing MIL framework in a plug-and-play manner with an acceptable computational cost.
• Score: 2.3364474984323103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embeds the instances into features via a pre-trained feature extractor, and an MIL aggregator that combines instance embeddings into predictions. Most efforts have typically focused on improving these parts. This involves refining the feature embeddings through self-supervised pre-training as well as modeling the correlations between instances separately. In this paper, we proposed a sparsely coding MIL (SC-MIL) method that addresses those two aspects at the same time by leveraging sparse dictionary learning. The sparse dictionary learning captures the similarities of instances by expressing them as sparse linear combinations of atoms in an over-complete dictionary. In addition, imposing sparsity improves instance feature embeddings by suppressing irrelevant instances while retaining the most relevant ones. To make the conventional sparse coding algorithm compatible with deep learning, we unrolled it into a sparsely coded module leveraging deep unrolling. The proposed SC module can be incorporated into any existing MIL framework in a plug-and-play manner with an acceptable computational cost. The experimental results on multiple datasets demonstrated that the proposed SC module could substantially boost the performance of state-of-the-art MIL methods. The codes are available at \href{https://github.com/sotiraslab/SCMIL.git}{https://github.com/sotiraslab/SCMIL.git}.

Related papers

• 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)
• 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)
• RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification [0.0]
  Whole slide image (WSI) classification is a critical task in computational pathology. Current methods rely on multiple-instance learning (MIL) models with frozen feature extractors. We show that our method outperforms state-of-the-art MIL models on weakly supervised classification tasks.
  arXiv  Detail & Related papers  (2023-10-03T09:59:59Z)
• Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification [11.996318969699296]
  Masked hard instance mining (MHIM-MIL) is presented. MHIM-MIL uses a Siamese structure (Teacher-Student) with a consistency constraint to explore potential hard instances. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that MHIM-MIL outperforms other latest methods in terms of performance and training cost.
  arXiv  Detail & Related papers  (2023-07-28T01:40:04Z)
• Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need [18.832471712088353]
  We propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting. We also propose an accurate pseudo label generation method through prototype learning.
  arXiv  Detail & Related papers  (2023-07-05T12:44:52Z)
• Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification [21.16848269555692]
  Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. We propose a novel framework called Iteratively Coupled MIL (ICMIL) which bridges the loss back-propagation process from the bag-level classifier to the patch embedder.
  arXiv  Detail & Related papers  (2023-03-28T06:12:53Z)
• 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)
• CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction [52.94486705393062]
  We go beyond typical multi-instance learning (MIL) framework and propose a novel contrastive instance learning (CIL) framework. Specifically, we regard the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance. Experiments demonstrate the effectiveness of our proposed framework, with significant improvements over the previous methods on NYT10, GDS and KBP.
  arXiv  Detail & Related papers  (2021-06-21T04:51:59Z)
• Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning [16.84711797934138]
  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.
  arXiv  Detail & Related papers  (2020-11-17T20:51:15Z)
• 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)
• Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies [60.285091454321055]
  We design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes.
  arXiv  Detail & Related papers  (2020-03-18T13:07:51Z)

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.