Pathology

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.

BACKGROUND: The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign an...

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the c...

Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis.

The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to...

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) im...

A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules.

PURPOSE: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and t...

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning.

We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a bi...

WBC-based segmentation and classification on microscopic images: a minor improvement.

Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBC...

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small ce...

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease.

Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with...

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

BACKGROUND: Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide pa...

BI-RADS-NET: AN EXPLAINABLE MULTITASK LEARNING APPROACH FOR CANCER DIAGNOSIS IN BREAST ULTRASOUND IMAGES.

In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This ...

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that ar...

Diagnostic armamentarium of infectious keratitis: A comprehensive review.

Infectious keratitis (IK) represents the leading cause of corneal blindness worldwide, particularly in developing countries. A good outcome of IK is c...

Fault Diagnosis of Permanent Magnet DC Motors Based on Multi-Segment Feature Extraction.

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. Only using the signal featur...

Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.

Cellularity estimation forms an important aspect of the visual examination of bone marrow biopsies. In clinical practice, cellularity is estimated by ...

Discriminating Neoplastic from Nonneoplastic Tissues Using an miRNA-Based Deep Cancer Classifier.

Next-generation sequencing has enabled the collection of large biological data sets, allowing novel molecular-based classification methods to be devel...

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet.

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose f...

Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging.

In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorit...

Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments.

Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of th...

Ultrasound deep learning for monitoring of flow-vessel dynamics in murine carotid artery.

Several arterial diseases are closely related with mechanical properties of the blood vessel and interactions of flow-vessel dynamics such as mean flo...