Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Zhou, Changjiang; Jin, Yi; Chen, Yuzong; Huang, Shan; Huang, Rengpeng; Wang, Yuhong; Zhao, Youcai; Yao, Chen; Guo, Lingchuan; Liao, Jun

doi:10.1016/j.compmedimag.2021.101861

Cited by 56 publications

(40 citation statements)

References 26 publications

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“…However, the qualities of histopathology slides from TCGA and hospitals were quite different. Hence, we could observe a variation in model performance [76]. In a few studies [45,46], the model performance was less as compared to other lung cancer classification models because of microscopic images from private hospitals with small sized datasets.…”

Section: Discussion and Future Directionsmentioning

confidence: 96%

“…Based on methods used in the literature, we categorized the segmentation methods into threshold-based, contour-and transform-based and clusterbased methods. In threshold-based approaches, use of Otsu's thresholding [21,26,32,41,43,44,52,54,74,76,81,85,89,92,95], incremental thresholding [59,60], and multiple thresholding [22,88] were reported for extraction of ROI. Contour-based segmentation includes active contour methods [29,59,101] and Sobel edge detectors [21].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…), autoencoders, and customized CNNs top the list. SVM [45,46,67,86,88,95,96,99,108] and RF tree classifier [45,76,80,82,84,86,103,108] were second and third in the list. In the 'Others' category, we included hashing, LR, quasi-supervised learning algorithm, ensemble learning, and linear classifier based on different features extracted from ROI for decision-making.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…The undetected rate of carcinoma, undetected rate of adenoma, and over-detected rate were 0%, 9.3%, and 9.9%, respectively for dataset 1 and 0%, 36.1%, and 27.1%, respectively, for dataset 2 [75]. Zhou et al [76] performed classification and localization of colon ADC using weakly supervised deep CNN methods. Gaussian blur operation and Otsu's thresholding were used in this study.…”

Section: Colon Adcmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists ′ evaluations.

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Section: Discussion and Future Directionsmentioning

confidence: 96%

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Colon Adcmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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“…Normal biopsies are more frequent than neoplastic and form a sizeable portion of the clinical workload. Therefore, it would be clinically beneficial to automate the separation of normal and neoplastic biopsies (4)(5)(6)(7). Artificial intelligence (AI) based diagnostic tools for histopathology pre-screening deployed in the digital pathology workflow is an unmet need of clinical research.…”

Section: Introductionmentioning

confidence: 99%

AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

Bilal

Tsang

Ali

et al. 2022

Preprint

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Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate the diagnostic decision-making and reduce the workload of pathologists, we present an AI-based pre-screening tool capable of identifying normal and neoplastic colon biopsies. To learn the differential histological patterns from whole slides images (WSIs) stained with hematoxylin and eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level labels and no detailed cell or region-level annotations. The proposed method was developed and validated on an internal cohort of biopsy slides (n=4 292) from two hospitals labeled with corresponding diagnostic categories assigned by pathologists after reviewing case reports. Performance of the proposed colon cancer pre-screening tool was evaluated in a cross-validation setting using the internal cohort (n=4 292) and also by an external validation on The Cancer Genome Atlas (TCGA) cohort (n=731). With overall cross-validated classification accuracy (AUROC = 0.9895) and external validation accuracy (AUROC = 0.9746), the proposed tool promises high accuracy to assist with the pre-screening of colorectal biopsies in clinical practice. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant pathological features. The proposed AI tool correctly reported some slides as neoplastic while clinical reports suggested they were normal. Additionally, we analyzed genetic mutations and gene enrichment analysis of AI-generated neoplastic scores to gain further insight into the model predictions and explore the association between neoplastic histology and genetic heterogeneity through representative genes and signaling pathways.

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Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Raju

Rao

2022

Concurrency and Computation

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Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

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Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Cited by 56 publications

References 26 publications

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

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