Pathology

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a stron...

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an ave...

An image classification deep-learning algorithm for shrapnel detection from ultrasound images.

Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation r...

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many...

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diag...

Development of a system for the automated identification of herbarium specimens with high accuracy.

Herbarium specimens are dried plants mounted onto paper. They are used by a limited number of researchers, such as plant taxonomists, as a source of i...

Robotic Revision of Hepaticojejunostomy for Benign Biliary Stricture.

Surgical revision of biliary enteric anastomoses (BEA) can be a challenging undertaking and a robotic platform may provide advantages that address man...

Nucleus classification in histology images using message passing network.

Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profilin...

Cross-tissue immune cell analysis reveals tissue-specific features in humans.

Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartme...

A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep l...

The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups.

AIMS: Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5...

Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning.

OBJECTIVE: To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) sampl...

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric ...

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm.

The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combine...

An improved CNN-based architecture for automatic lung nodule classification.

Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung ...

Automatic microscopic diagnosis of diseases using an improved UNet++ architecture.

Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system ...

Fluorescence Analysis of Circulating Exosomes for Breast Cancer Diagnosis Using a Sensor Array and Deep Learning.

Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer...

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the surviva...

Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.

BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable mot...

Molecular Visualization of Early-Stage Acute Kidney Injury with a DNA Framework Nanodevice.

DNA nanomachines with artificial intelligence have attracted great interest, which may open a new era of precision medicine. However, their in vivo be...