AIMC Topic: Pathologists

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The future of pathology is digital.

Pathology, research and practice Jun 20, 2020
Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by t...
Workflow Pathologists Image Interpretation, Computer-Assisted Pathology Humans Artificial Intelligence Image Processing, Computer-Assisted
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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology May 29, 2020
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...
Lymphoma Pathologists Image Interpretation, Computer-Assisted Lymphoma, Follicular Lymphoma, Large B-Cell, Diffuse Pseudolymphoma Observer Variation Humans Diagnosis, Computer-Assisted Histological Techniques Neural Networks, Computer Deep Learning Algorithms
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Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc May 28, 2020
Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of patho...
Social Media Pathologists Pathology Humans Algorithms Deep Learning
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Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.

Annals of diagnostic pathology Apr 12, 2020
Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integr...
Fibrosis Azo Compounds Collagen Pathologists Supervised Machine Learning Non-alcoholic Fatty Liver Disease Clinical Trials as Topic Organ Dysfunction Scores Eosine Yellowish-(YS) Methyl Green Automation Portal Vein Practice Patterns, Physicians' Artificial Intelligence Severity of Illness Index Image Processing, Computer-Assisted Liver Cirrhosis Liver Biopsy Humans
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Software-assisted decision support in digital histopathology.

The Journal of pathology Feb 25, 2020
Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the dail...
Image Interpretation, Computer-Assisted Pathology Software Algorithms Pathologists Humans Artificial Intelligence
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Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.

Cancer cytopathology Feb 3, 2020
BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspiration biopsy (FNAB). Each category has an associated risk of malignancy, which is important in the ...
Humans ROC Curve Pathologists Cytodiagnosis Thyroid Gland Machine Learning Biopsy, Fine-Needle Thyroid Neoplasms Algorithms Adenocarcinoma, Follicular Thyroid Nodule Reproducibility of Results
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Reply to the letter to the editor: 'Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images'.

European journal of cancer (Oxford, England : 1990) Jan 30, 2020
Humans Melanoma Pathologists Skin Neoplasms Deep Learning
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Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Oct 23, 2019
Pathologists Melanoma Skin Neoplasms Deep Learning Humans
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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Observer Variation Skin Neoplasms Nevus Reproducibility of Results Biopsy Diagnosis, Computer-Assisted Humans Pathologists Deep Learning Melanoma Microscopy Diagnosis, Differential Image Interpretation, Computer-Assisted Predictive Value of Tests
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Pathologist-level classification of histopathological melanoma images with deep neural networks.

European journal of cancer (Oxford, England : 1990) May 23, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% ...
Nevus Predictive Value of Tests Microscopy Biopsy Reproducibility of Results Humans Image Interpretation, Computer-Assisted Pathologists Diagnosis, Computer-Assisted Melanoma Observer Variation Deep Learning Skin Neoplasms
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