AIMC Topic: Pathologists

Clear Filters Showing 81 to 90 of 127 articles

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Journal of biomedical informatics Aug 3, 2020
Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is tedious and time-consuming. In recent years, a long list of machine learning approaches to image classi...
Colorectal Neoplasms Social Responsibility Pathologists Humans Neural Networks, Computer Machine Learning
View on PubMed DOI

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...
Pathologists Image Interpretation, Computer-Assisted Pathology Artificial Intelligence Image Processing, Computer-Assisted Workflow Humans
View on PubMed DOI

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 ...
Humans Neural Networks, Computer Deep Learning Lymphoma Pathologists Algorithms Lymphoma, Large B-Cell, Diffuse Diagnosis, Computer-Assisted Lymphoma, Follicular Image Interpretation, Computer-Assisted Pseudolymphoma Observer Variation Histological Techniques
View on PubMed DOI

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...
Algorithms Deep Learning Social Media Humans Pathologists Pathology
View on PubMed DOI

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...
Methyl Green Clinical Trials as Topic Organ Dysfunction Scores Eosine Yellowish-(YS) Fibrosis Azo Compounds Collagen Automation Portal Vein Pathologists Humans Practice Patterns, Physicians' Supervised Machine Learning Artificial Intelligence Non-alcoholic Fatty Liver Disease Severity of Illness Index Liver Cirrhosis Image Processing, Computer-Assisted Liver Biopsy
View on PubMed DOI

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...
Software Image Interpretation, Computer-Assisted Algorithms Humans Pathologists Artificial Intelligence Pathology
View on PubMed DOI

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 ...
Thyroid Neoplasms Thyroid Nodule ROC Curve Pathologists Cytodiagnosis Biopsy, Fine-Needle Algorithms Reproducibility of Results Thyroid Gland Humans Machine Learning Adenocarcinoma, Follicular
View on PubMed DOI

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
Melanoma Pathologists Skin Neoplasms Deep Learning Humans
View on PubMed DOI

Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Oct 23, 2019
Humans Melanoma Skin Neoplasms Deep Learning Pathologists
View on PubMed DOI

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...
Image Interpretation, Computer-Assisted Observer Variation Nevus Deep Learning Diagnosis, Differential Skin Neoplasms Humans Predictive Value of Tests Reproducibility of Results Biopsy Melanoma Diagnosis, Computer-Assisted Pathologists Microscopy
View on PubMed DOI
Popular Topics
Recent Journals