CWC-transformer: a visual transformer approach for compressed whole slide image classification

Wang, Yaowei; Guo, Jing; Ye, Yun; Kang, Yan; Xia, Yuelong; Li, Zhenhui; Duan, Yongchun; Wang, Kelong

doi:10.1007/s00521-022-07857-3

Cited by 9 publications

(14 citation statements)

References 42 publications

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“…In [6], a two-phase approach has been developed for classifying Whole Slide Images (WSI) of weakly supervised learning. It uses contrastive learning to train the feature extractor in the compression stage.…”

Section: Resultsmentioning

confidence: 99%

“…In analysing the articles that mention the use of transformers [6], [9], [18], [24], [25], [26], we observed various implementations and significant impacts in the field of medical image analysis. In the article [6], the CWC-Transformer model was developed for WSI classification, combining contrastive learning with CNNs and transformers. This approach stands out for its ability to capture both local and global information from the images, providing a richer and more detailed analysis.…”

Section: B Transformersmentioning

confidence: 99%

“…Model Used Accuracy AUC USBC breast cancer [27] TGA-MIL [5] 77.0% 75.6% Camelyon16 [28]/ MSK [29] CWC Transformer [6] 89.1%/ 92.6% 93.9%/ 94.7% Camelyon16 [28] DSMIRL [8] 89.9% 95.3% ICIAR Grand Challenge 2018…”

Section: Datasetmentioning

confidence: 99%

“…PatchCamelyon [36] LGViT [24] 91.8% -BreakHis [31] GMIL [17] 96.4% -99.8% 99.7% -98.6% [37] TGA-MIL [5] 92.7% 98.3% Tcga-Lung [38] CWC Transformer [6] 92.6% 94.7%/ NSCLC [39]/ ILD [40] NSK [7] -59.0% -89.0%/ 59.0% Pneumonia CT DSMIRL [8] 93.0% 96.7% HyperKvasir [41] & LDPolypVideo [42] CSM [9] -98.4%…”

Section: Datasetmentioning

confidence: 99%

“…In [5], the implementation of the TGA-MIL model reveals challenges such as the need to fine-tune for different tissues and the dependence on image quality. Similarly, [6] with its CWC-Transformer model highlights the high consumption of computational resources, especially with high magnification images, and potential problems in treating similar patches such as negative pairs. The study [7] on the detection of lung diseases faces limitations due to the restricted size of the databases, compromising the generalizability of the results.…”

Section: E Limitationsmentioning

confidence: 99%

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Multiple Instance Learning in Medical Images: A Systematic Review

Barbosa,

Ferreira,

Junior

et al. 2024

IEEE Access

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This article presents a systematic review of Multiple Instance Learning (MIL) applied to image classification, specifically highlighting its applications in medical imaging. Motivated by the need for a comprehensive and up-to-date analysis due to the scarcity of recent reviews, this study uses defined selection criteria to systematically assess the quality and synthesize data from relevant studies. Focusing on MIL, a subfield of machine learning that deals with learning from sets of instances or "bags", this review is crucial for medical diagnosis, where accurate lesion detection is a challenge. The review details the methodologies, advances and practical implementations of MIL, emphasizing the attention-grabbing and transformative mechanisms that improve the analysis of medical images. Challenges such as the need for extensive annotated datasets and significant computational resources are discussed. In addition, the review covers three main topics: the characterization of MIL algorithms in various imaging domains, a detailed evaluation of performance metrics, and a critical analysis of data structures and computational resources. Despite these challenges, MIL offers a promising direction for research with significant implications for medical diagnostics, highlighting the importance of continued exploration and improvement in this area.

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Section: Resultsmentioning

confidence: 99%