A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches

Li, Chen; Li, Xintong; Rahaman, Md Mamunur; Li, Xiaoyan; Sun, Hongzan; Zhang, Hong; Zhang, Yong; Li, Xiaoqi; Wu, Jihuai; Yao, Yu-Dong; Grzegorzek, Marcin

doi:10.48550/arxiv.2102.10553

Cited by 3 publications

(6 citation statements)

References 156 publications

(311 reference statements)

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“…A review of such approaches is beyond the scope of this paper. We refer the reader to recent comprehensive reviews for more insights on AI and deep learning in computational pathology [26]- [29], with specific reviews for histology [30]- [34] and cytology [35]- [37]. If some ethical issues have appeared in the use of computational pathology in clinical routine [38], most pathologists are in favor of their use [39].…”

Section: Computational Pathologymentioning

confidence: 99%

“…As previously mentioned, clinical laboratories have to follow guidelines for quality control and both DP and CP have to conform to these to be usable in practice. As a result, quality control is an emerging topic in DP and CP [26], [35], [48]- [50]. In the next sections, we review the approaches proposed to assess the quality of WSIs.…”

Section: E Quality Control In Digital Pathologymentioning

confidence: 99%

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Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

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Section: Computational Pathologymentioning

confidence: 99%

Section: E Quality Control In Digital Pathologymentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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“…Histopathology image analysis (HIA) is an important task in modern medicine and is the gold standard for cancer detection and treatment planning [17]. The development of whole slide image (WSI) scanners has enabled the digitization of tissue biopsies into gigapixel images and paved the way for the application of machine learning techniques in the field of digital pathology [3,22]. However, employing popular convolutional neural network (CNN) architectures for varied tasks in HIA is non trivial and has several challenges, ranging from the large size of WSIs and extreme high resolution to lack of precise labeling and stain color variations [22].…”

Section: Introductionmentioning

confidence: 99%

“…The development of whole slide image (WSI) scanners has enabled the digitization of tissue biopsies into gigapixel images and paved the way for the application of machine learning techniques in the field of digital pathology [3,22]. However, employing popular convolutional neural network (CNN) architectures for varied tasks in HIA is non trivial and has several challenges, ranging from the large size of WSIs and extreme high resolution to lack of precise labeling and stain color variations [22]. This motivates the need for memory efficient methods that mitigate the need for fine-grained labels and are fairly interpretable.…”

Section: Introductionmentioning

confidence: 99%

Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification

Chikontwe¹,

Nam²,

Go³

et al. 2022

Preprint

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Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods. To address this, multiple instance learning (MIL) is a popular method that poses classification as a weakly supervised learning task with slide-level labels only. While current MIL methods apply variants of the attention mechanism to re-weight instance features with stronger models, scant attention is paid to the properties of the data distribution. In this work, we propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We assume that in binary MIL, positive bags have larger feature magnitudes than negatives, thus we can enforce the model to maximize the discrepancy between bags with a metric feature loss that models positive bags as out-of-distribution. To achieve this, unlike existing MIL methods that use single-batch training modes, we propose balanced-batch sampling to effectively use the feature loss i.e., (+/-) bags simultaneously. Further, we employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder. Experimental results on existing benchmark datasets show our approach is effective and improves over state-of-the-art MIL methods. https://github.com/PhilipChicco/FRMIL

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“…The advent of whole slide image (WSI) scanners, which convert the tissue on the biopsy slide into a gigapixel image fully preserving the original tissue structure [1], provides a good opportunity for the application of deep learning in the field of digital pathology [2,3,4]. However, the deep learning based biopsy diagnosis in WSI has to face a great challenges due to the huge size and the lack of pixel-level annotations [5].…”

Section: Introductionmentioning

confidence: 99%

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

Shao¹,

Bian²,

Chen³

et al. 2021

Preprint

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Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided a proof for convergence. Based on this framework, we devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL can effectively deal with unbalanced/balanced and binary/multiple classification with great visualization and interpretability. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence compared with state-ofthe-art methods. The test AUC for the binary tumor classification can be up to 93.09% over CAMELYON16 dataset. And the AUC over the cancer subtypes classification can be up to 96.03% and 98.82% over TCGA-NSCLC dataset and TCGA-RCC dataset, respectively.

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A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches

Cited by 3 publications

References 156 publications

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

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