Pathology

Latest AI and machine learning research in pathology for healthcare professionals.

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A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer ...

Application of machine learning in depression risk prediction for connective tissue diseases.

This study retrospectively collected clinical data from 480 patients with connective tissue diseases...

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for effic...

Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.

BACKGROUND: Deep learning (DL) has set new standards in cancer diagnosis, significantly enhancing th...

A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction.

RATIONALE AND OBJECTIVES: In the USA over 1 million breast biopsies are performed annually. Approxim...

Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.

Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring sy...

Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study.

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly in...

Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole-Slide Histopathology Images: A Retrospective Multicenter Study.

Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response...

DP-CLAM: A weakly supervised benign-malignant classification study based on dual-angle scanning ultrasound images of thyroid nodules.

In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achi...

Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study.

BACKGROUND: Artificial intelligence (AI) is set to shape the future of medical practice. The perspec...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting mil...

Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology.

PURPOSE: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph no...

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.

PURPOSE: To create a system to enable the identification of histological variants of bladder cancer ...

Amphotericin B tissue penetration and pharmacokinetics in healthy and -infected rats: insights from microdialysis and population modeling.

INTRODUCTION: This study evaluated the relationship between total plasma and free kidney concentrati...

Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.

To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels <...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is cruc...

Brain tumour histopathology through the lens of deep learning: A systematic review.

PROBLEM: Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more comple...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to stai...

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for i...

Boosting skin cancer diagnosis accuracy with ensemble approach.

Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective ...

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