Dermatology

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available da...

Measurement of psoriasis-affected area with artificial neural network.

Dermoscopic image segmentation based on Pyramid Residual Attention Module.

We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automati...

Classification of Skin Cancer Lesions Using Explainable Deep Learning.

Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need a...

Artificial intelligence for oral cancer diagnosis: What are the possibilities?

Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advan...

Acne Detection by Ensemble Neural Networks.

Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical ...

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks.

Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and ...

Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images.

INTRODUCTION: The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with diffe...

Artificial intelligence in the detection of skin cancer.

Recent advances in artificial intelligence (AI) in dermatology have demonstrated the potential to improve the accuracy of skin cancer detection. These...

Background selection schema on deep learning-based classification of dermatological disease.

Skin diseases are one of the most common ailments affecting humans. Artificial intelligence based on deep learning can significantly improve the effic...

Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application.

In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional sol...

Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT.

Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive br...

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.

The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions,...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose di...

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most bel...

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

BACKGROUND In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to dis...

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.

BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require man...

Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging.

BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the...

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have bec...

Novel aspects of Raman spectroscopy in skin research.

The analytical technology of Raman spectroscopy has an almost 100-year history. During this period, many modifications and developments happened in th...