Pathology

Deep learning for necrosis detection using canine perivascular wall tumour whole slide images.

Necrosis seen in histopathology Whole Slide Images is a major criterion that contributes towards scoring tumour grade which then determines treatment options. However conventional manual assessment suffers from inter-operator reproducibility impacting grading precision. To address this, automatic necrosis detection using AI may be used to assess necrosis for final scoring that contributes towards ...

Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study.

BACKGROUND: The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services.

Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these method...

On the role of deep learning model complexity in adversarial robustness for medical images.

BACKGROUND: Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the inp...

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed t...

A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning.

As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional ...

Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagus.

BACKGROUND AND AIMS: The risk of progression in Barrett's esophagus (BE) increases with development of dysplasia. There is a critical need to improve ...

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward develop...

Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with ...

Deep Learning-Based CT Imaging for the Diagnosis of Liver Tumor.

The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liv...

Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage...

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and...

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams.

Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with hu...

Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model.

Raman spectroscopy is a powerful method for estimating the molecular structure of a target that can be adapted for biomedical analysis given its non-d...

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation.

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant dif...

A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.

Application of deep learning methods to transcriptomic data has the potential to enhance the accuracy and efficiency of tissue classification and cell...

Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm.

This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and ...

AI-based structure prediction empowers integrative structural analysis of human nuclear pores.

INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by the two membranes of the nuclear envelope. Nuclear pore complexes (NPCs) p...

Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold.

INTRODUCTION The nuclear pore complex (NPC) is the molecular conduit in the nuclear membrane of eukaryotic cells that regulates import and export of b...

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study.

OBJECTIVE: Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are bo...