Gigapixel Whole-Slide Images Classification using Locally Supervised Learning

• URL: http://arxiv.org/abs/2207.08267v1
• Date: Sun, 17 Jul 2022 19:31:54 GMT
• Title: Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
• Authors: Jingwei Zhang, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras
• Abstract summary: Histo whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. Conventional methods rely on a multiple instance learning (MIL) strategy to process a WSI at patch level. We propose a locally supervised learning framework which processes the entire slide by exploring the entire local and global information.
• Score: 31.213316201151954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tools for processing WSIs is challenging due to their enormous sizes. Currently, to deal with this issue, conventional methods rely on a multiple instance learning (MIL) strategy to process a WSI at patch level. Although effective, such methods are computationally expensive, because tiling a WSI into patches takes time and does not explore the spatial relations between these tiles. To tackle these limitations, we propose a locally supervised learning framework which processes the entire slide by exploring the entire local and global information that it contains. This framework divides a pre-trained network into several modules and optimizes each module locally using an auxiliary model. We also introduce a random feature reconstruction unit (RFR) to preserve distinguishing features during training and improve the performance of our method by 1% to 3%. Extensive experiments on three publicly available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority of our method on different classification tasks. Our method outperforms the state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times faster. Additionally, when dividing it into eight modules, our method requires as little as 20% of the total gpu memory required by end-to-end training. Our code is available at https://github.com/cvlab-stonybrook/local_learning_wsi.

Related papers

• Enhancing Visual Continual Learning with Language-Guided Supervision [76.38481740848434]
  Continual learning aims to empower models to learn new tasks without forgetting previously acquired knowledge. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. Specifically, we use PLMs to generate semantic targets for each class, which are frozen and serve as supervision signals.
  arXiv  Detail & Related papers  (2024-03-24T12:41:58Z)
• 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)
• Multi-Level Contrastive Learning for Dense Prediction Task [59.591755258395594]
  We present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. Our method consistently outperforms the recent state-of-the-art methods on various datasets with significant margins.
  arXiv  Detail & Related papers  (2023-04-04T17:59:04Z)
• A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images [12.961686610789416]
  Self-supervised learning (SSL) has emerged as an alternative solution to reduce the annotation overheads in whole slide images. These SSL approaches are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features. We propose a Dual-branch SSL Framework for WSI tumour segmentation (DSF-WSI) that can effectively learn image features from multi-resolution WSIs.
  arXiv  Detail & Related papers  (2023-03-20T10:57:28Z)
• Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning [48.02011627390706]
  We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
  arXiv  Detail & Related papers  (2023-03-02T15:18:58Z)
• Giga-SSL: Self-Supervised Learning for Gigapixel Images [0.8029049649310211]
  Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. The current state-of-the-art (SoTA) approach to classify WSI subdivides them into tiles, encodes them by pre-trained networks and applies Multiple Instance Learning (MIL) to train for specific downstream tasks. Here, we propose a strategy of slide level SSL to leverage the large number of WSI without annotations to infer powerful slide representations.
  arXiv  Detail & Related papers  (2022-12-06T19:09:19Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• 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)
• Memory Efficient Meta-Learning with Large Images [62.70515410249566]
  Meta learning approaches to few-shot classification are computationally efficient at test time requiring just a few optimization steps or single forward pass to learn a new task. This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken. We propose LITE, a general and memory efficient episodic training scheme that enables meta-training on large tasks composed of large images on a single GPU.
  arXiv  Detail & Related papers  (2021-07-02T14:37:13Z)
• Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification [7.876654642325896]
  We propose an end-to-end framework that clusters the patches from a Whole Slide Images (WSI) into $k$-groups, samples $k'$ patches from each group for training, and uses an adaptive attention mechanism for slide level prediction. The framework is optimized end-to-end on slide-level cross-entropy, patch-level cross-entropy, and KL-divergence loss.
  arXiv  Detail & Related papers  (2021-03-19T04:24:01Z)
• Solving Mixed Integer Programs Using Neural Networks [57.683491412480635]
  This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each.
  arXiv  Detail & Related papers  (2020-12-23T09:33:11Z)

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.