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Pathologists

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Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

Toxicologic pathology
We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary...

Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations.

Scientific reports
The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotatin...

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

Histopathology
AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (...

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Journal of imaging informatics in medicine
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in wh...

Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.

Scientific reports
The main bottleneck in training a robust tumor segmentation algorithm for non-small cell lung cancer (NSCLC) on H&E is generating sufficient ground truth annotations. Various approaches for generating tumor labels to train a tumor segmentation model ...

Unveiling the risks of ChatGPT in diagnostic surgical pathology.

Virchows Archiv : an international journal of pathology
ChatGPT, an AI capable of processing and generating human-like language, has been studied in medical education and care, yet its potential in histopathological diagnosis remains unexplored. This study evaluates ChatGPT's reliability in addressing pat...

Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy.

European urology oncology
Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; however, interobserver variability remains a challenge. Artificial intelligence algorithms applied to histopathologic images standardize grading, but most...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

The journal of pathology. Clinical research
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. W...

A survey analysis of the adoption of large language models among pathologists.

American journal of clinical pathology
OBJECTIVES: We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists.

Thinking like a pathologist: Morphologic approach to hepatobiliary tumors by ChatGPT.

American journal of clinical pathology
OBJECTIVES: This research aimed to evaluate the effectiveness of ChatGPT in accurately diagnosing hepatobiliary tumors using histopathologic images.