PreMix: Boosting Multiple Instance Learning in Digital Histopathology through Pre-training with Intra-Batch Slide Mixing

• URL: http://arxiv.org/abs/2408.01162v1
• Date: Fri, 2 Aug 2024 10:24:35 GMT
• Title: PreMix: Boosting Multiple Instance Learning in Digital Histopathology through Pre-training with Intra-Batch Slide Mixing
• Authors: Bryan Wong, Mun Yong Yi,
• Abstract summary: PreMix extends the general MIL framework by pre-training the MIL aggregator with an intra-batch slide mixing approach. It achieves a mean of 4.7% performance improvement over the baseline MIL framework. Ultimately, PreMix paves the way for more efficient and accurate WSI classification under limited WSI-labeled datasets.
• Score: 2.6703221234079946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The classification of gigapixel-sized whole slide images (WSIs), digital representations of histological slides obtained via a high-resolution scanner, faces significant challenges associated with the meticulous and time-consuming nature of fine-grained labeling. While weakly-supervised multiple instance learning (MIL) has emerged as a promising approach, current MIL methods are constrained by their limited ability to leverage the wealth of information embedded within unlabeled WSIs. This limitation often necessitates training MIL feature aggregators from scratch after the feature extraction process, hindering efficiency and accuracy. PreMix extends the general MIL framework by pre-training the MIL aggregator with an intra-batch slide mixing approach. Specifically, PreMix incorporates Barlow Twins Slide Mixing during pre-training, enhancing its ability to handle diverse WSI sizes and maximizing the utility of unlabeled WSIs. Combined with Mixup and Manifold Mixup during fine-tuning, PreMix achieves a mean of 4.7% performance improvement over the baseline MIL framework, the hierarchical image pyramid transformer (HIPT), on the Camelyon16 dataset. The observed improvement across a range of active learning acquisition functions and WSI-labeled training budgets highlights the framework's adaptability to diverse datasets and varying resource constraints. Ultimately, PreMix paves the way for more efficient and accurate WSI classification under limited WSI-labeled datasets, encouraging the broader adoption of unlabeled WSI data in histopathological research. The code is available at https://anonymous.4open.science/r/PreMix

Related papers

• Rethinking Pre-trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classification [2.6703221234079946]
  Multiple instance learning (MIL) has become a preferred method for gigapixel whole slide image (WSI) classification without requiring patch-level annotations. This study systematically evaluating MIL feature extractors across three dimensions: pre-training dataset, backbone model, and pre-training method. Our findings reveal that selecting a robust self-supervised learning (SSL) method has a greater impact on performance than relying solely on an in-domain pre-training dataset.
  arXiv  Detail & Related papers  (2024-08-02T10:34:23Z)
• Fast Semisupervised Unmixing Using Nonconvex Optimization [80.11512905623417]
  We introduce a novel convex convex model for semi/library-based unmixing. We demonstrate the efficacy of Alternating Methods of sparse unsupervised unmixing.
  arXiv  Detail & Related papers  (2024-01-23T10:07:41Z)
• PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis [71.8946280170493]
  This paper introduces PowMix, a versatile embedding space regularizer that builds upon the strengths of unimodal mixing-based regularization approaches. PowMix is integrated before the fusion stage of multimodal architectures and facilitates intra-modal mixing, such as mixing text with text, to act as a regularizer.
  arXiv  Detail & Related papers  (2023-12-19T17:01:58Z)
• Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification [13.286360560353936]
  Whole slide image (WSI) classification requires repetitive zoom-in and out for pathologists, as only small portions of the slide may be relevant to detecting cancer. Due to the lack of patch-level labels, multiple instance learning (MIL) is a common practice for training a WSI classifier. One of the challenges in MIL for WSIs is the weak supervision coming only from the slide-level labels, often resulting in severe overfitting. Our approach augments the training dataset by sampling a subset of patches in the WSI without significantly altering the underlying semantics of the original slides.
  arXiv  Detail & Related papers  (2023-11-29T09:18:39Z)
• Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification [18.679580844360615]
  We propose a new Pseudo-bag Mixup (PseMix) data augmentation scheme to improve the training of MIL models. Our scheme generalizes the Mixup strategy for general images to special WSIs via pseudo-bags. It is designed as an efficient and decoupled method, neither involving time-consuming operations nor relying on MIL model predictions.
  arXiv  Detail & Related papers  (2023-06-28T13:02:30Z)
• 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)
• Harnessing Hard Mixed Samples with Decoupled Regularizer [69.98746081734441]
  Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. In this paper, we propose an efficient mixup objective function with a decoupled regularizer named Decoupled Mixup (DM) DM can adaptively utilize hard mixed samples to mine discriminative features without losing the original smoothness of mixup.
  arXiv  Detail & Related papers  (2022-03-21T07:12:18Z)
• SMILE: Self-Distilled MIxup for Efficient Transfer LEarning [42.59451803498095]
  In this work, we propose SMILE - Self-Distilled Mixup for EffIcient Transfer LEarning. With mixed images as inputs, SMILE regularizes the outputs of CNN feature extractors to learn from the mixed feature vectors of inputs. The triple regularizer balances the mixup effects in both feature and label spaces while bounding the linearity in-between samples for pre-training tasks.
  arXiv  Detail & Related papers  (2021-03-25T16:02:21Z)
• SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data [124.95585891086894]
  Proposal is called Semantically Proportional Mixing (SnapMix) It exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. Our method consistently outperforms existing mixed-based approaches.
  arXiv  Detail & Related papers  (2020-12-09T03:37:30Z)
• Sequence-Level Mixed Sample Data Augmentation [119.94667752029143]
  This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems. Our approach, SeqMix, creates new synthetic examples by softly combining input/output sequences from the training set.
  arXiv  Detail & Related papers  (2020-11-18T02:18:04Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

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.