Dermatology

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measu...

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of predict...

Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet.

Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagn...

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-ce...

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportuni...

Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and...

Comparative assessment of CNN architectures for classification of breast FNAC images.

Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It a...

Attention Residual Learning for Skin Lesion Classification.

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Althou...

Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim...

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible thr...

Identification of a closed cutaneous injury after mechanical trauma caused by collision.

PURPOSE: Robotics has evolved rapidly in terms of mechanical design and control in the past few years. Collaborative robots that have direct contact w...

Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features.

To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and t...

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks.

Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires ve...

Extracellular Vesicles Released by () Promote Disease Progression and Induce the Production of Different Cytokines in Macrophages and B-1 Cells.

The extracellular vesicles (EVs) released by can contribute to the establishment of infection and host immunomodulation. In this study, we characteri...

Automated detection of erythema migrans and other confounding skin lesions via deep learning.

Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acu...

Serum Procalcitonin and Presepsin Levels in Patients with Generalized Pustular Psoriasis.

Patients with generalized pustular psoriasis (GPP) often present with symptoms that must be differentiated from sepsis. Procalcitonin (PCT) and presep...

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioura...

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine.

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin i...

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks.

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple ...

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine le...