Pathology

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples.

BACKGROUND AND OBJECTIVES: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. ...

Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many ...

Re-evaluating the diagnostic efficacy of PSA as a referral test to detect clinically significant prostate cancer in contemporary MRI-based image-guided biopsy pathways.

INTRODUCTION: Modern image-guided biopsy pathways at diagnostic centres have greatly refined the investigations of men referred with suspected prostat...

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands ...

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy.

Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identif...

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images.

Whole-Slide Histopathology Image (WSI) is generally considered the gold standard for cancer diagnosis and prognosis. Given the large inter-operator va...

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation.

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medi...

Deep learning for abdominal adipose tissue segmentation with few labelled samples.

PURPOSE: Fully automated abdominal adipose tissue segmentation from computed tomography (CT) scans plays an important role in biomedical diagnoses and...

Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study.

BACKGROUND: A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to ad...

Design and Characterization of Liposomal Methotrexate and Its Effect on BT-474 Breast Cancer Cell Line.

Breast cancer is the most common type of cancer among women worldwide. Traditional treatments, including chemotherapy, surgery, mastectomy, and radio...

Development of a Machine learning image segmentation-based algorithm for the determination of the adequacy of Gram-stained sputum smear images.

BACKGROUND: Machine learning (ML) prepares and trains a model through supervised or unsupervised learning methods. Sputum, a respiratory tract secreti...

Physics-based learning with channel attention for Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. ...

Reducing retraction forces with tactile feedback during robotic total mesorectal excision in a porcine model.

Excessive tissue-instrument interaction forces during robotic surgery have the potential for causing iatrogenic tissue damages. The current in vivo st...

Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver.

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry....

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.

This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the di...

Multiview confocal super-resolution microscopy.

Confocal microscopy remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, bu...

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of...

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions.

OBJECTIVES: To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LD...

[Robot-assisted Minimally Invasive Surgery in the age of surgical data science].

With the continuous development of information technology, robotics and data science will certainly have a similar impact on invasive medicine over th...

Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different expe...