Pathology

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualize...

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks.

Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simulta...

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly...

Cell Image Classification: A Comparative Overview.

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding t...

Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies.

OBJECTIVE: Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. M...

Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer.

BACKGROUND: Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratu...

Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging.

BACKGROUND AND PURPOSE: Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radio...

Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks.

Thyroid cancer is a disease in which the first symptom is a nodule in the thyroid region of the neck. It is one of the cancers with the highest incide...

SDCT-AuxNet: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis.

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural netw...

A deep learning approach to radiation dose estimation.

Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly...

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images.

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data pres...

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspi...

Computational analysis of morphological and molecular features in gastric cancer tissues.

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of m...

Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternativ...

Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration.

Advances in two two-photon microscopy (2PM) have made three-dimensional (3D) neural imaging of deep cortical regions possible. However, 2PM often suff...

Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility.

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility ...

Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy.

Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but e...

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.

BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation.

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorde...

Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.

A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robus...