Pathology

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists.

Decoding finger movement patterns from microscopic neural drive information based on deep learning.

Recent development of surface electromyogram (sEMG) decomposition technique provides a good basis of decoding movements from individual motor unit (MU) activities that directly representing microscopic neural drives. How to interpret the function and contribution of each decomposed MU to macroscopic movements remains unclear. The objective of this study is to decode finger movement patterns by est...

Characterization of the Impacts of Living at High Altitude in Taif: Oxidative Stress Biomarker Alterations and Immunohistochemical Changes.

At high elevations, the human body experiences a number of pathological, physiological, and biochemical changes, all of which have adverse impacts on ...

Effect of prostate and bony pelvic dimensions measured by preoperative magnetic resonance imaging on robot-assisted radical prostatectomy.

Bone pelvic dimensions and body habitus may have effects on robot-assisted radical prostatectomy (RARP). In this study, we examined the effects of bod...

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of ...

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replac...

Deep learning for automatic diagnosis of gastric dysplasia using whole-slide histopathology images in endoscopic specimens.

BACKGROUND: Distinguishing gastric epithelial regeneration change from dysplasia and histopathological diagnosis of dysplasia is subject to interobser...

Rapid, automated nerve histomorphometry through open-source artificial intelligence.

We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from ligh...

A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach.

Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is diffi...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease...

Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC.

BACKGROUND: Programmed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immun...

Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm.

Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formid...

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning.

The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative med...

Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction.

Rhabdomyosarcoma (RMS), the most common malignant soft tissue tumor in children, has several histologic subtypes that influence treatment and predict ...

Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf Zr O Thin Films.

By controlling the configuration of polymorphic phases in high-k Hf Zr O thin films, new functionalities such as persistent ferroelectricity at an ext...

Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its u...

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the ...

Functionalized Spiral-Rolling Millirobot for Upstream Swimming in Blood Vessel.

Untethered small robots with multiple functions show considerable potential as next-generation catheter-free systems for biomedical applications. Howe...

Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images.

In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether p...

Application and performance of artificial intelligence technology in cytopathology.

Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent adva...