Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images

• URL: http://arxiv.org/abs/2403.14346v2
• Date: Wed, 09 Oct 2024 13:49:42 GMT
• Title: Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images
• Authors: Yujian Liu, Ruoxuan Wu, Xinjie Shen, Zihuang Lu, Lingyu Liang, Haiyu Zhou, Shipu Xu, Shaoai Cai, Shidang Xu,
• Abstract summary: We introduce the Concentric Dual Fusion Attention-MIL (CDFA-MIL) framework. CDFA-MIL combines point-to-area feature-colum attention and point-to-point concentric-row attention using concentric patch. Its application has demonstrated exceptional performance, significantly surpassing existing MIL methods in accuracy and F1 scores on prominent datasets.
• Score: 2.428210413498989
• License:
• Abstract: In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data. However, current methods fall short in bridging the domain gap between pretrained models and medical imaging, and often fail to account for spatial relationships across different magnifications. Addressing these challenges, we introduce the Concentric Dual Fusion Attention-MIL (CDFA-MIL) framework,which innovatively combines point-to-area feature-colum attention and point-to-point concentric-row attention using concentric patch. This approach is designed to effectively fuse correlated information, enhancing feature representation and providing stronger correlation guidance for WSI analysis. CDFA-MIL distinguishes itself by offering a robust fusion strategy that leads to superior WSI recognition. Its application has demonstrated exceptional performance, significantly surpassing existing MIL methods in accuracy and F1 scores on prominent datasets like Camelyon16 and TCGA-NSCLC. Specifically, CDFA-MIL achieved an average accuracy and F1-score of 93.7\% and 94.1\% respectively on these datasets, marking a notable advancement over traditional MIL approaches.

Related papers

• SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion [59.96233305733875]
  Time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations. This paper presents an efficient-based model, the Series-cOre Fused Time Series forecaster (SOFTS)
  arXiv  Detail & Related papers  (2024-04-22T14:06:35Z)
• AMMUNet: Multi-Scale Attention Map Merging for Remote Sensing Image Segmentation [4.618389486337933]
  We propose AMMUNet, a UNet-based framework that employs multi-scale attention map merging. The proposed AMMM effectively combines multi-scale attention maps into a unified representation using a fixed mask template. We show that our approach achieves remarkable mean intersection over union (mIoU) scores of 75.48% on the Vaihingen dataset and an exceptional 77.90% on the Potsdam dataset.
  arXiv  Detail & Related papers  (2024-04-20T15:23:15Z)
• Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing [5.990692497580643]
  Fus-MAE is a self-supervised learning framework based on masked autoencoders. Our empirical findings demonstrate that Fus-MAE can effectively compete with contrastive learning strategies tailored for SAR-optical data fusion.
  arXiv  Detail & Related papers  (2024-01-05T11:36:21Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation [3.1002416427168304]
  This thesis focuses on retinal blood vessel segmentation tasks. It provides an extensive literature review of deep learning-based medical image segmentation approaches. It proposes a novel efficient, simple multiview learning framework.
  arXiv  Detail & Related papers  (2023-11-02T06:31:08Z)
• Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration [67.23451452670282]
  Misalignments between multi-modality images pose challenges in image fusion. We propose a Cross-modality Multi-scale Progressive Dense Registration scheme. This scheme accomplishes the coarse-to-fine registration exclusively using a one-stage optimization.
  arXiv  Detail & Related papers  (2023-08-22T03:46:24Z)
• Learning multi-domain feature relation for visible and Long-wave Infrared image patch matching [39.88037892637296]
  We present the largest visible and Long-wave Infrared (LWIR) image patch matching dataset, termed VL-CMIM. In addition, a multi-domain feature relation learning network (MD-FRN) is proposed.
  arXiv  Detail & Related papers  (2023-08-09T11:23:32Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• Searching a Compact Architecture for Robust Multi-Exposure Image Fusion [55.37210629454589]
  Two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. This study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19% improvement in PSNR for general scenarios and an impressive 23.5% enhancement in misaligned scenarios.
  arXiv  Detail & Related papers  (2023-05-20T17:01:52Z)
• Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance [148.9832328803202]
  We model the infor-mation fusion within focal stack via graph networks. We build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
  arXiv  Detail & Related papers  (2021-10-02T00:54:39Z)
• Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images [24.35779077001839]
  We propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations. We introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism.
  arXiv  Detail & Related papers  (2020-01-09T07:47:51Z)

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.