Pathology

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

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Mask R-CNN assisted diagnosis of spinal tuberculosis.

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inade...

Liquid biopsy for Renal Cell Carcinoma: A comprehensive review of techniques, applications, and future prospects.

Liquid biopsy techniques have developed rapidly in recent years and demonstrated success in cancer d...

Urinary TYROBP and HCK as genetic biomarkers for non-invasive diagnosis and therapeutic targeting in IgA nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, but its pathogenesis remains...

An overview of artificial intelligence based automated diagnosis in paediatric dentistry.

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent...

An integrated microflow cytometry platform with artificial intelligence capabilities for point-of-care cellular phenotype analysis.

The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyp...

Deep Learning-Enabled Rapid Metabolic Decoding of Small Extracellular Vesicles via Dual-Use Mass Spectroscopy Chip Array.

The increasing focus of small extracellular vesicles (sEVs) in liquid biopsy has created a significa...

MCBERT: A multi-modal framework for the diagnosis of autism spectrum disorder.

Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD) emerges as a disti...

CXCL12 impact on glioblastoma cells behaviors under dynamic culture conditions: Insights for developing new therapeutic approaches.

Glioblastoma multiforme (GBM) is the most prevalent malignant brain tumor, with an average survival ...

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles su...

Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to t...

Neural networks for predicting etiological diagnosis of uveitis.

BACKGROUND/OBJECTIVES: The large number and heterogeneity of causes of uveitis make the etiological ...

Interactively Fusing Global and Local Features for Benign and Malignant Classification of Breast Ultrasound Images.

OBJECTIVE: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its a...

Deep-learning-based image compression for microscopy images: An empirical study.

With the fast development of modern microscopes and bioimaging techniques, an unprecedentedly large ...

Comparative transcriptomic and molecular biology analyses to explore potential immune responses to challenge in .

is a significant pathogen affecting shrimp and crab farming, particularly strains carrying genes as...

Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images.

BACKGROUND AND AIMS: The levels of M2 macrophages are significantly associated with the prognosis of...

Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of...

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