AIMC Topic: Pathologists

Clear Filters Showing 51 to 60 of 129 articles

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

The American journal of pathology
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...

A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

Histopathology
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-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports
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...

Identifying pathological slices of gastric cancer via deep learning.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND: The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, ...

[Artificial intelligence: a solution for the lack of pathologists?].

Der Pathologe
Given the rapid developments, there is no doubt that artificial intelligence (AI) will substantially impact pathological diagnostics. However, it remains an open question if AI will primarily be another diagnostic tool, such as immunohistochemistry, ...

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may t...

Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images.

Journal of clinical pathology
AIMS: Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for ...

Pathologist-Patient Visits-Revisited.

The American journal of surgical pathology
Direct pathologist-patient encounters are infrequent, but there has been a modest movement toward such interactions in the past 2 decades. The present article places that movement in perspective. It includes a discussion of diverse factors-including ...

Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide images.

The Journal of dermatological treatment
BACKGROUND: Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans.