Dermatology

Progressive Multiple Sclerosis Transcriptome Deconvolution Indicates Increased M2 Macrophages in Inactive Lesions.

Accumulating evidence suggests M2 macrophages contribute to tissue reparation and limit inflammation in multiple sclerosis (MS). However, most studies have focused on murine models without substantial support through human MS observations. The present study aimed to quantify the relative abundances of M2 macrophages in different lesion types excised from human MS patients. CIBERSORTx, an establish...

Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs.

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area f...

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning.

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to ...

Automatic skin lesion classification based on mid-level feature learning.

Dermoscopic images are widely used for melanoma detection. Many existing works based on traditional classification methods and deep learning models ha...

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most ...

Comparing the capabilities of transfer learning models to detect skin lesion in humans.

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion h...

Melanoma detection using adversarial training and deep transfer learning.

Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem...

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stag...

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images.

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABC...

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce me...

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study.

Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor...

Gene Expression Profile Prediction in Uveal Melanoma Using Deep Learning: A Pilot Study for the Development of an Alternative Survival Prediction Tool.

BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis.

We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-sup...

Integrative blockwise sparse analysis for tissue characterization and classification.

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we devel...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally diffi...

Breast ultrasound region of interest detection and lesion localisation.

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised brea...

An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine.

Skin is the outermost and largest organ of the human body that protects us from the external agents. Among the various types of diseases affecting the...

CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings.

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance image...

A deep learning system for differential diagnosis of skin diseases.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower di...

A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver.

The purpose of thermal ablation is induction of tumor death by means of localized hyperthermia resulting in irreversible cellular damage. Ablative the...