Pathology

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has ...

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable ine...

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning.

Particle agglutination assays are widely adopted immunological tests that are based on antigen-antib...

Deep-learning on-chip light-sheet microscopy enabling video-rate volumetric imaging of dynamic biological specimens.

Volumetric imaging of dynamic signals in a large, moving, and light-scattering specimen is extremely...

Artificial intelligence for solid tumour diagnosis in digital pathology.

Tumour diagnosis relies on the visual examination of histological slides by pathologists through a m...

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography.

The objective of the study was to determine if the pathology depicted on a mammogram is either benig...

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical ...

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks.

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biolo...

Toxicity evaluation and oxidative stress response of fumaronitrile, a persistent organic pollutant (POP) of industrial waste water on tilapia fish (Oreochromis mossambicus).

The study was designed to determine the impact of acute toxicity of fumaronitrile exposure through t...

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. ...

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

Although deep learning networks applied to digital images have shown impressive results for many pat...

Deep learning models for benign and malign ocular tumor growth estimation.

Relatively abundant availability of medical imaging data has provided significant support in the dev...

Predictive constitutive modelling of arteries by deep learning.

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 ...

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may ass...

Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.

Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropi...

Eco-friendly estimation of isosorbide dinitrate and hydralazine hydrochloride using Green Analytical Quality by Design-based UPLC Method.

Isosorbide dinitrate (ISD) and hydralazine hydrochloride (HDZ) are critical drugs for the treatment ...

Re: Bernardo Rocco, Luca Sarchi, Simone Assumma, et al. Digital Frozen Sections with Fluorescence Confocal Microscopy During Robot-assisted Radical Prostatectomy: Surgical Technique. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2021.03.021.

Reply to Eoin Dinneen, Jon Oxley, and Greg Shaw's Letter to the Editor re: Bernardo Rocco, Luca Sarchi, Simone Assumma, et al. Digital Frozen Sections with Fluorescence Confocal Microscopy During Robot-assisted Radical Prostatectomy: Surgical Technique. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2021.03.021.

Hyperspectral imaging and artificial intelligence to detect oral malignancy - part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network.

BACKGROUND: Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, g...

Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions.

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, ...

Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures.

BACKGROUND: Millions of people throughout the world suffer from parasite infections. Traditionally, ...