Pathology

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpretation. The aim of this study is to test the feasibility of a system combining the use of a low-magnification, wider field-of-view pCLE probe and a com...

Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain-fre...

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.

High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational resea...

Growth Factor Delivery for the Repair of a Critical Size Tibia Defect Using an Acellular, Biodegradable Polyethylene Glycol-Albumin Hydrogel Implant.

Growth factor delivery using acellular matrices presents a promising alternative to current treatment options for bone repair in critical-size injurie...

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Pa...

A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.

In digital mammography, which is used for the early detection of breast tumors, oversight may occur due to overlap between normal tissues and lesions....

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional...

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amou...

Automated classification of histopathology images using transfer learning.

Early and accurate diagnosis of diseases can often save lives. Diagnosis of diseases from tissue samples is done manually by pathologists. Diagnostics...

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorit...

Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network.

Breast cancer is the most common cancer among women worldwide with about half a million cases reported each year. Mammary thermography can offer early...

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue propert...

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learn...

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automate...

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides.

IMPORTANCE: Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slid...

Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data.

We present a novel AI-based approach to the few-shot automated segmentation of mitochondria in large-scale electron microscopy images. Our framework l...

Automated label-free detection of injured neuron with deep learning by two-photon microscopy.

Stroke is a significant cause of morbidity and long-term disability globally. Detection of injured neuron is a prerequisite for defining the degree of...

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between t...

Toward Automated Bladder Tumor Stratification Using Confocal Laser Endomicroscopy.

Urothelial carcinoma of the bladder (UCB) is the most common urinary cancer. White-light cystoscopy (WLC) forms the corner stone for the diagnosis of...

Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels.

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucia...