Skin Cancer

An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.

Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a highe...

A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwid...

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...

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 sk...

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 artific...

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...

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.

Artificial intelligence for melanoma diagnosis.

Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, whi...

Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design.

Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designi...

Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) ...

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large ...

Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been consi...

Review of medical image recognition technologies to detect melanomas using neural networks.

BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class pro...

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagn...

Value of MR-based radiomics in differentiating uveal melanoma from other intraocular masses in adults.

PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) r...

Piloting a Deep Learning Model for Predicting Nuclear BAP1 Immunohistochemical Expression of Uveal Melanoma from Hematoxylin-and-Eosin Sections.

BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Monosom...

Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.

The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastati...

Case of slowly progressive type 1 diabetes mellitus with drastically reduced insulin secretory capacity after immune checkpoint inhibitor treatment for advanced renal cell carcinoma.

We encountered a 55-year-old Japanese man with advanced renal cell carcinoma and slowly progressive ...

Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians.

In recent years the concept of precision medicine has become a popular topic particularly in medical...

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 ...

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

Dermoscopic images are widely used for melanoma detection. Many existing works based on traditional ...