Dermatology

A Deep Learning Approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium.

Optical photons undergo strong scattering when propagating beyond 1-mm deep inside biological tissue. Finding the origin of these diffused optical wavefronts is a challenging task. Breaking through the optical diffusion limit, photoacoustic (PA) imaging (PAI) provides high-resolution and label-free images of human vasculature with high contrast due to the optical absorption of hemoglobin. In real-...

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constitute more than 90% of diagnoses, we demonstrate that the majority of dermatopathology work is amenable to automatic machine analysis. A major feature o...

The RoScan Thermal 3D Body Scanning System: Medical Applicability and Benefits for Unobtrusive Sensing and Objective Diagnosis.

The RoScan is a novel, high-accuracy multispectral surface scanning system producing colored 3D models that include a thermal layer. (1) Background: a...

A nanoemulsion-based nanogel of essential oil with leishmanicidal activity against and .

Cutaneous leishmaniasis is one of the diseases that severely affects human skin. Nanogels are the well-known formulation for topical drug delivery due...

Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System.

PURPOSE:  A recently developed deep learning model (U-Net) approximated the clinical performance of radiologists in the prediction of clinically signi...

Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.

Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagn...

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pi...

Use of an Artificial Neural Network to Identify Patient Clusters in a Large Cohort of Patients with Melanoma by Simultaneous Analysis of Costs and Clinical Characteristics.

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis.

This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with ac...

Deep learning based classification of facial dermatological disorders.

Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one ...

Artificial intelligence for melanoma diagnosis.

Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition throu...

Ischemic Lesion Segmentation using Ensemble of Multi-Scale Region Aligned CNN.

The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion from radiological images for effective treatment plan...

Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks.

Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus and associated with substantial morbid...

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to devel...

Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading.

Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (C...

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular i...

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of s...

Spatiotemporal identification of druggable binding sites using deep learning.

Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence ...

Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet.

PURPOSE: Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In...

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The mos...