Pathology

Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are difficult to confirm overfitting problems, etc.). In our study, we propose a CAD system for tumor diagnosis using an image fusion method combined with ...

Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer.

Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in t...

Classification of glomerular hypercellularity using convolutional features and support vector machine.

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerula...

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the dis...

Nodular Thyroid Disease in the Era of Precision Medicine.

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and sev...

An improved deep network for tissue microstructure estimation with uncertainty quantification.

Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with...

The Antifungal Peptide MCh-AMP1 Derived From Inhibits Growth via Inducing ROS Generation and Altering Fungal Cell Membrane Permeability.

The rise of antifungal drug resistance in species responsible for life threatening candidiasis is considered as an increasing challenge for the publi...

Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.

Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognize...

Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues.

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this techn...

Liver tissue classification of en face images by fractal dimension-based support vector machine.

Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer d...

Enabling In-Bore MRI-Guided Biopsies With Force Feedback.

Limited physical access to target organs of patients inside an MRI scanner is a major obstruction to real-time MRI-guided interventions. Traditional t...

Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study.

The locus coeruleus (LC) has a long-established role in the attentional and arousal response to threat, and in the emergence of pathological anxiety i...

Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning.

Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot c...

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging.

PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor...

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence.

Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. C...

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.

BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic ...

Machine learning derived input-function in a dynamic F-FDG PET study of mice.

Tracer kinetic modelling, based on dynamic F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in hum...

Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning.

OBJECTIVE: The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinom...

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.

This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multi...

Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model.

We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate ...