Artificial intelligence for pathology

Xing, Fuyong; Zhang, Xuhong; Cornish, Toby C.

doi:10.1016/b978-0-12-821259-2.00011-9

Cited by 6 publications

(3 citation statements)

References 233 publications

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“…The algorithms for mass detection in mammography [ 57 ], lung cancer screening with computed tomography [ 58 ], and the detection of colonic polyps in CT [ 59 ] colonography are the most widely explored CAD algorithm applications. The latest target is COVID-19 diagnosis support [ 60 ] or digital pathology [ 61 ].…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.

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Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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“…This technology is enabled by virtual (digital) microscopy. Using digital pathology imaging not only permits the sharing of images between different locations for educational, research, and/or diagnostic purposes but also allows for the quantitative analysis of tissue morphology [22,23]. The use of digital slides offers several other advantages over glass slide review, such as fidelity of diagnostic details, portability, ease of sharing and storing, as well as the ability to utilize computer-aided diagnostic (CAD) tools.…”

Section: Itcsmentioning

confidence: 99%

Domain Generalization Across Multi-centre Digital Pathology Images for Lymph Node Segmentation and Classification

Dambandkhameneh

2024

Preprint

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Within the last few years, medical deep learning has experienced rapid advances, especially in the area of image interpretation. Using whole-slide images of histological lymph node sections, this thesis develops deep learning models and techniques for detecting and classifying breast cancer metastases. Pathologists would normally have to perform extensive microscopic assessments on this clinically relevant task. In patients with breast cancer, metastases in lymph nodes have therapeutic implications. So, an automated solution may remarkably reduce pathologists’ workload while at the same time reducing the subjectivity of their diagnoses. There are, however, still several significant challenges associated with the development and translation of medical deep learning systems. To begin with, developing large and well-annotated datasets is expensive and most often, the labels are imbalanced. In addition, it is difficult to transfer the performance of deep learning algorithms from one dataset and setting to another due to domain shift issues. Lastly, the results of deep learning systems should be comprehensible and applicable to clinical datasets. In order to enhance effectiveness in an unknown target domain and increase generalization, this thesis assesses ensemble learning by transferring prior knowledge from non-medical and medical sources. Although deep learning methods may be successful, they may not perform well in a clinical workflow. Many datasets are constructed from millions of patches of images, leading to a data curation bias; others only contain annotations at the slide-level, making it difficult to detect errors at the local level as long as the results are correct. As a way of alleviating the class imbalance and biased training data, this thesis proposes a cluster-based sampling method for whole-slide histopathology image analysis. With the proposed ensemble learning and sampling methods, cutting-edge machine learning architectures can be extended, and state-of-the-art performance can be achieved for both diagnostic test images and whole slide images. Results in the sampling part show that using the same dataset, the results are roughly the same, regardless of sampling methods. However, the result of the unseen dataset falls dramatically when it is sampled randomly. By sampling separately, we reduce the probability of drops in the results for unseen datasets. Random selection gives a sensitivity of 0.82 on an unseen dataset, however sampling separately gives a sensitivity of 0.93. In random selection, dice index similarity is 0.83, whereas in sampling separately, dice index similarity is 0.90. Also, observing the slide level results in St. Michael’s hospital dataset, it can be concluded that we cannot decide about the best model and all the models give good results at least in one slide. So, combining the results from multiple configurations would give a more reliable and consistent result.

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“…Meanwhile,along with the progress in the micrography and whole-slide scanning technology, the pathological slides can be retained in the form of digital images nowadays, enabling wide applications of the computer vision technology in the field of computer-assisted pathological diagnosis. However, even if deep learning technology has shown great promise in the field image analysis, it faces a series of unique challenges when applied in the field of pathological image analysis (Pinckaers et al 2020;Rijthoven et al 2020;Xing et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

Liao

Zheng

et al. 2021

Preprint

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The pathological diagnosis is the gold standard for neoplasms and their precursors, which is highly relevant to the treatment planning and the prognostic analysis. Currently, deep learning networks have been used for the pathological computer-assisted diagnosis and treatment decision-makings. However, due to extremely large size of the whole slide images (WSIs) of pathological slides, the prevailing deep learning models are un-applicable directly in the WSIs analysis. Moreover, the precise exclusion of the blank regions and interfere regions, as well as the manual annotation of various lesioned and normal regions in super large WSIs are infeasible and unavailable in clinical practice. To address aforementioned problems, we develop an computer-aided decision-making system based on multimodal and multi-instance deep convolution networks (CNN) to assist in the diagnosis and treatment of endometrial atypical hyperplasia (AH)/ endometrial intraepithelial hyperplasia (EIH). Firstly, we set up the frame-work of computer-aided decision-making system based on the WSIs image patterns of AH/EIH, and then transfer the large-scale WSI analysis to the small-scale analysis of multiple suspected lesion regions which can be accomplished the major computer vision models, and eventually the results of prognostic analysis for multiple small-scale suspected lesion regions are summarized to obtain the prognostic results of WSIs by the decision supporting algorithm based on the cognition intelligence. We validate the method via experimental analysis of 102 endometrial atypical hyperplasia patients at the West China Second University Hospital of Sichuan University. The performance achieved for endometrial AH/EIH prognostic analysis includes accuracy (85.3%), precision (84.6%), recall (86.3%). Meanwhile, the method has superior performance to prognostic judgment of a single pathologist as well as approximates to analysis results determined by three pathologists according to the majority voting method.

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Artificial intelligence for pathology

Cited by 6 publications

References 233 publications

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Domain Generalization Across Multi-centre Digital Pathology Images for Lymph Node Segmentation and Classification

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

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Artificial intelligence for pathology

Cited by 6 publications

References 233 publications

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Domain Generalization Across Multi-centre Digital Pathology Images for Lymph Node Segmentation and Classification

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal &amp; Multi-Instance Deep Convolution Networks

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Product

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Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks