Pathology

Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy.

Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these limitations. In this work, we developed a new methodology for rapid, label-free molecular phenotyping of biological fluids (e.g., blood) by exploitin...

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images.

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. ...

Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic chang...

Deep neural network models for computational histopathology: A survey.

Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and...

Virus identification in electron microscopy images by residual mixed attention network.

BACKGROUND AND OBJECTIVE: Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis ...

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent ...

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and ...

Pollen analysis using multispectral imaging flow cytometry and deep learning.

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollinati...

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images.

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine t...

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomog...

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture.

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According ...

Deriving new soft tissue contrasts from conventional MR images using deep learning.

Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully expl...

Machine-assisted interpretation of auramine stains substantially increases through-put and sensitivity of microscopic tuberculosis diagnosis.

Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throu...

Digital Biopsy with Fluorescence Confocal Microscope for Effective Real-time Diagnosis of Prostate Cancer: A Prospective, Comparative Study.

BACKGROUND: A microscopic analysis of tissue is the gold standard for cancer detection. Hematoxylin-eosin (HE) for the reporting of prostate biopsy (P...

Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.

The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome....

A comparison of the efficacy, safety, and duration of frame-based and Remebot robot-assisted frameless stereotactic biopsy.

OBJECTIVE: The aim of this study was to compare the efficacy, safety, and duration of Remebot robot-assisted frameless brain biopsy with those of stan...

AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images.

The scanning laser ophthalmoscopy (SLO) has become an important tool for the determination of peripheral retinal pathology, in recent years. However, ...

Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma.

Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characteri...

The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues.

Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. Th...

Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence.

Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal rad...