MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification

• URL: http://arxiv.org/abs/2408.12825v1
• Date: Fri, 23 Aug 2024 04:08:30 GMT
• Title: MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification
• Authors: Mingxi Ouyang, Yuqiu Fu, Renao Yan, ShanShan Shi, Xitong Ling, Lianghui Zhu, Yonghong He, Tian Guan,
• Abstract summary: Multiple instance learning (MIL) is a promising weakly supervised learning approach for WSI classification. We introduce a feature augmentation technique, MergeUp, which merges bags with low-priority bags to enhance inter-category information. Experimental results on the CAMELYON-16, BRACS, and TCGA-LUNG datasets demonstrate the superiority of our method over existing state-of-the-art approaches.
• Score: 1.2387547097768696
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of WSIs and the scarcity of manual annotations present substantial challenges. Multiple instance learning (MIL) is a promising weakly supervised learning approach for WSI classification. Recently research revealed employing pseudo bag augmentation can encourage models to learn various data, thus bolstering models' performance. While directly inheriting the parents' labels can introduce more noise by mislabeling in training. To address this issue, we translate the WSI classification task from weakly supervised learning to semi-weakly supervised learning, termed SWS-MIL, where adaptive pseudo bag augmentation (AdaPse) is employed to assign labeled and unlabeled data based on a threshold strategy. Using the "student-teacher" pattern, we introduce a feature augmentation technique, MergeUp, which merges bags with low-priority bags to enhance inter-category information, increasing training data diversity. Experimental results on the CAMELYON-16, BRACS, and TCGA-LUNG datasets demonstrate the superiority of our method over existing state-of-the-art approaches, affirming its efficacy in WSI classification.

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)
• An efficient framework based on large foundation model for cervical cytopathology whole slide image screening [13.744580492120749]
  We propose an efficient framework for cervical cytopathology WSI classification using only WSI-level labels through unsupervised and weakly supervised learning. Experiments conducted on the CSD and FNAC 2019 datasets demonstrate that the proposed method enhances the performance of various MIL methods and achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-07-16T08:21:54Z)
• A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification [43.00429513188971]
  Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. We propose a universal knowledge embedded contrastive learning framework (KnowCL) for supervised, unsupervised, and semisupervised HSI classification.
  arXiv  Detail & Related papers  (2024-04-02T06:24:21Z)
• 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)
• ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification [5.836559246348487]
  Pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. This paper proposes a novel scheme, ProtoDiv, using a bag prototype to guide the division of WSI pseudo-bags.
  arXiv  Detail & Related papers  (2023-04-13T16:27:08Z)
• Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification [10.243293283318415]
  Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification. We propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. Our framework is evaluated on five pathology WSI datasets on various WSI heads.
  arXiv  Detail & Related papers  (2023-03-15T08:41:57Z)
• Novel Class Discovery in Semantic Segmentation [104.30729847367104]
  We introduce a new setting of Novel Class Discovery in Semantic (NCDSS) It aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image. We propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels.
  arXiv  Detail & Related papers  (2021-12-03T13:31:59Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

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.