Pathology

Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images.

BACKGROUND AND AIMS: Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded lesions using endoscopic images acquired during real clinical settings.

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.

The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermor...

Application of Computed Tomography Imaging in Diagnosis of Endocrine Nerve of Gastric Cancer and Nursing Intervention Effect.

In this article, some parameters and characteristics of computed tomography (CT) images in patients with gastric cancer are analyzed and the applicati...

Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging.

PURPOSE: We aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on...

Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks.

Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision h...

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site...

Localization and recognition of leukocytes in peripheral blood: A deep learning approach.

Automatic recognition and classification of leukocytes helps medical practitioners to diagnose various blood-related diseases by analysing their perce...

Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).

In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases...

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic...

Complete abdomen and pelvis segmentation using U-net variant architecture.

PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmenta...

Developing a Machine Learning Algorithm for Identifying Abnormal Urothelial Cells: A Feasibility Study.

INTRODUCTION: Urine cytology plays an important role in diagnosing urothelial carcinoma (UC). However, urine cytology interpretation is subjective and...

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.

The intra-axonal water exchange time (τ), a parameter associated with axonal permeability, could be an important biomarker for understanding and treat...

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease a...

Technical Note: Automatic segmentation of CT images for ventral body composition analysis.

PURPOSE: Body composition is known to be associated with many diseases including diabetes, cancers, and cardiovascular diseases. In this paper, we dev...

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of docto...

Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs.

Facial photographs of the subjects are often used in the diagnosis process of orthognathic surgery. The aim of this study was to determine whether con...

4D deep learning for real-time volumetric optical coherence elastography.

PURPOSE: Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have...

Potential use of deep learning techniques for postmortem imaging.

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries....

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture.

Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recen...

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks.

Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and...