Pathology

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, ...

Diagnosis of invasive fungal infections: challenges and recent developments.

BACKGROUND: The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the ch...

Hemodynamic study of blood flow in the aorta during the interventional robot treatment using fluid-structure interaction.

An interventional robot is a means for vascular diagnosis and treatment, and it can perform dredging, releasing drug and operating. Normal hemodynamic...

How should the best human embryo in vitro be? Current and future challenges for embryo selection.

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical...

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography rei...

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy...

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment sele...

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed f...

Machine learning in computational histopathology: Challenges and opportunities.

Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The...

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in i...

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma.

Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications ha...

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance.

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence...

A multi-modal deep neural network for multi-class liver cancer diagnosis.

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network ...

DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell...

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopatho...

Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images.

Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-l...

Artificial intelligence-aided optical imaging for cancer theranostics.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-makin...

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to...

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through sili...

An Integrated Approach to Improve the Assay Performance of Quantum Dot-Based Lateral Flow Immunoassays by Using Silver Deposition.

Traditional quantum dot-based lateral flow immunoassay (QD-LFIA) is limited to signal loss in part by the blinking, photobleaching and oxidative quenc...