AIMC Topic: Pathologists

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Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

Scientific reports May 24, 2023
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs o...
Humans Artificial Intelligence Pathologists Semantics Neoplasms Deep Learning
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Quantitative assessment of myocardial fibrosis by digital image analysis: An adjunctive tool for pathologist "ground truth".

Cardiovascular pathology : the official journal of the Society for Cardiovascular Pathology Apr 29, 2023
AIMS: Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic relevance. We aimed to assess if the complementary use of an automated artificial intelligence soft...
Humans Fibrosis Myocardium Artificial Intelligence Pathologists Reproducibility of Results
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Explainability and causability in digital pathology.

The journal of pathology. Clinical research Apr 12, 2023
The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis ar...
Humans Image Processing, Computer-Assisted Pathologists Artificial Intelligence Algorithms
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[Impact of digital pathology implementation in Reunion Island].

Bulletin du cancer Feb 15, 2023
In recent decades, the major scientific advances in oncology have complexified anatomic pathology practice. Collaboration with local and national pathologists is essential for ensuring a high-quality diagnosis. Anatomic pathology is undergoing a digi...
Reunion Humans Artificial Intelligence Pathologists Telepathology
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Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.

Pathology, research and practice Feb 6, 2023
Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic e...
Artificial Intelligence Algorithms Humans Microscopy Databases, Factual Pathologists
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Applications of artificial intelligence in prostate cancer histopathology.

Urologic oncology Jan 11, 2023
The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, work...
Pathologists Biopsy, Large-Core Needle Male Prostatic Neoplasms Humans Neural Networks, Computer Artificial Intelligence
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Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Jan 11, 2023
Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provi...
Pathologists Machine Learning Humans Software Artificial Intelligence
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Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

The American journal of pathology Dec 21, 2022
Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of CRC and shape the patients' follow-up plans. This stu...
Deep Learning Adenocarcinoma Pathologists Hyperplasia Colorectal Neoplasms Male Humans Neural Networks, Computer Female
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A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

Histopathology Sep 12, 2022
AIMS: The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artif...
Deep Learning Lung Neoplasms Adenocarcinoma of Lung Workflow Humans Pathologists Artificial Intelligence
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Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports Sep 6, 2022
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in b...
Neoplasm Grading Breast Neoplasms Survival Analysis Pathologists Deep Learning Humans Observer Variation Female
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