Pathology

Comparison between two programs for image analysis, machine learning and subsequent classification.

In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the firs...

Diagnosis of focal liver lesions from ultrasound using deep learning.

PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning.

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to ...

Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy.

OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest ra...

Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.

Basic functions of living organisms are governed by the nervous system through bidirectional signals transmitted from the brain to neural networks. Th...

Detection and characterization of MRI breast lesions using deep learning.

PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions us...

Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.

The nematode is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a m...

Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.

Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we...

Can We Accurately Identify Peritoneal Metastases Based on Their Appearance? An Assessment of the Current Practice of Intraoperative Gastrointestinal Cancer Staging.

BACKGROUND: Peritoneal lesions are common findings during operative abdominal cancer staging. The decision to perform biopsy is made subjectively by t...

Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape.

Artificial intelligence (AI) has made impressive strides recently in interpreting complex images, thanks to improvements in deep learning techniques a...

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It ...

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and...

Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucop...

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.

PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ul...

A dense multi-path decoder for tissue segmentation in histopathology images.

BACKGROUND AND OBJECTIVE: Segmenting different tissue components in histopathological images is of great importance for analyzing tissues and tumor en...

Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models.

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multipho...

Convolutional neural network for cell classification using microscope images of intracellular actin networks.

Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there ...

Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes.

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's e...

Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.

Semen quality assessed by sperm count and sperm cell characteristics such as morphology and motility, is considered to be the main determinant of men'...

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that ...