Pathology

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

BACKGROUND: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste products from human food consumption, the creation of bile, the regulation of metabolic activities, the cleaning of the blood by sensitizing digestive m...

A Semi-supervised Deep Learning Method for Cervical Cell Classification.

Currently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and microbial infections. However, this technique entirely relies on manual operation and doctors' naked eye observation, resulting in a heavy workload and low accuracy rate. Recently, automatic pathological diagnosis has been developed to solve this probl...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosi...

Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.

Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the co...

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means o...

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms ...

MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease.

Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer type...

Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cell...

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling.

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial p...

Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images.

BACKGROUND & AIMS: Hepatocellular nodular lesions (HNLs) constitute a heterogeneous group of disorders. Differential diagnosis among these lesions, es...

Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.

Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determini...

Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care.

For years, hepatologists have been seeking non-invasive methods able to detect significant liver fibrosis. However, no previous algorithm using routin...

Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology.

The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by usin...

Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning.

Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this ...

Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset.

OBJECTIVE: Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and...

A Tissue Adhesion-Controllable and Biocompatible Small-Scale Hydrogel Adhesive Robot.

Recently, the realization of minimally invasive medical interventions on targeted tissues using wireless small-scale medical robots has received an in...

Hepatic enzymes and immunoinflammatory response to Bio-C Temp bioceramic intracanal medication implanted into the subcutaneous tissue of rats.

Our purpose was to evaluate the biocompatibility and hepatotoxicity of a new bioceramic intracanal medicament, Bio-C Temp (BIO). The biological proper...

An Optimized Framework for Breast Cancer Classification Using Machine Learning.

Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are...

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.

Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses eith...

The ethical challenges of artificial intelligence-driven digital pathology.

Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new po...