Sécuriser le diagnostic en anatomie et cytologie pathologiques en 2011. L’erreur diagnostique : entre discours et réalité

Bellocq, Jean‐Pierre; Anger, E.; Camparo, Philippe; Capron, Frédérique; Chenard, Marie–Pierre; Chetritt, Jérôme; Chigot, Jean-Paul; Cochand‐Priollet, Béatrix; Coindre, Jean‐Michel; Copin, Marie‐Christine; Fléjou, Jean‐François; Galateau, F; Gaulard, Philippe; Guiu, Michel; Michiels, Jean‐François; Saint‐André, Jean‐Paul; Scoazec, Jean‐Yves; Vacher-Lavenu, Marie-Cécile

doi:10.1016/j.annpat.2011.08.006

Cited by 7 publications

(6 citation statements)

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“…Biopsies, in turn, take more time to provide a result due to the acquisition procedure (e.g., fine-needle aspiration or open surgical biopsy), the tissue processing (creation of slide with the staining process), and finally, pathologist visual analysis. Naturally, pathologist analysis is a highly specialized and time-consuming task prone to inter and intra-observer discordance [3].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

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Section: Introductionmentioning

confidence: 99%

“…This question aims at identifying which types of tissues or structures can be identified using ML methods. 3. What are the trends that have been dominating HI analysis?…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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“…fine-needle aspiration or open surgical biopsy), the tissue processing (creation of slide with the staining process) and finally pathologist visual analysis. Naturally, pathologist analysis is a highly specialized and time-consuming task prone to inter and intra-observer discordance [3]. Furthermore, the staining process can cause the variance in the process of analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

Matos¹,

Ataky²,

Britto³

et al. 2021

Preprint

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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is not only time and resource consuming, but also very challenging even for experienced pathologists, resulting in inter-and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. In addition, we present a list of publicly available and private datasets that have been used in HI research.

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“…fine needle aspiration or surgical open biopsy); tissue processing (creation of the slide with the staining process); and a final analysis of the slide by a pathologist. Pathologist analysis is a highly specialized and time-consuming task prone to inter and intra-observer discordance [2]. The variance in the analysis process can be caused by the staining process by Hematoxylin and Eosin (H&E), which is the most common and accessible stain, but it can produce different color intensities depending on the brand, the storage time, and temperature.…”

Section: Introductionmentioning

confidence: 99%

Texture CNN for Histopathological Image Classification

Matos¹,

Britto

Oliveira

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.

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Sécuriser le diagnostic en anatomie et cytologie pathologiques en 2011. L’erreur diagnostique : entre discours et réalité

Cited by 7 publications

References 0 publications

Machine Learning Methods for Histopathological Image Analysis: A Review

Machine Learning Methods for Histopathological Image Analysis: A Review

Machine Learning Methods for Histopathological Image Analysis: A Review

Texture CNN for Histopathological Image Classification

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