Pathology

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early p...

Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy.

Rodent progressive cardiomyopathy (PCM) encompasses a constellation of microscopic findings commonly seen as a spontaneous background change in rat and mouse hearts. Primary histologic features of PCM include varying degrees of cardiomyocyte degeneration/necrosis, mononuclear cell infiltration, and fibrosis. Mineralization can also occur. Cardiotoxicity may increase the incidence and severity of P...

Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such as urethane or N-nitroso-N-methylurea to test stu...

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the deve...

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and ...

An end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification.

Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to l...

Machine Learning Techniques for Ophthalmic Data Processing: A Review.

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approac...

Residual breast tissue after robot-assisted nipple sparing mastectomy.

INTRODUCTION: While the long-term oncologic safety of robot-assisted nipple sparing mastectomy (RNSM) remains to be elucidated, histologically detecte...

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (M...

Artificial Intelligence for Classification of Soft-Tissue Masses at US.

PURPOSE: To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commo...

Super resolution microscopy and deep learning identify Zika virus reorganization of the endoplasmic reticulum.

The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flavivi...

Artificial intelligence applications for oncological positron emission tomography imaging.

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Rad...

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.

Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is ...

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classi...

Cytotoxic potentials of silibinin assisted silver nanoparticles on human colorectal HT-29 cancer cells.

It is of interest to study the cytotoxicity of silibinin assisted silver nanoparticles in human colorectal (HT-29) cancer cells. Silver nanoparticles ...

Deep Learning-Based Spermatogenic Staging Assessment for Hematoxylin and Eosin-Stained Sections of Rat Testes.

In preclinical toxicology studies, a "stage-aware" histopathological evaluation of testes is recognized as the most sensitive method to detect effects...

Extraction of the molecular level biomedical event trigger based on gene ontology using radial belief neural network techniques.

Detection of molecular level biomedical event extraction plays a vital role in creating and visualizing the applications related to natural language p...

Machine learning techniques for mitoses classification.

BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures ar...

Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine.

In this study, a transfer learning method was utilized to recognize and classify benign and malignant breast tumors, using two-dimensional breast ultr...

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary gla...