AIMC Topic: Skin Neoplasms

Clear Filters Showing 521 to 530 of 540 articles

[Clinical image identification of basal cell carcinoma and pigmented nevi based on convolutional neural network].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
To construct an intelligent assistant diagnosis model based on the clinical images of basal cell carcinoma (BCC) and pigmented nevi in Chinese by using the advanced convolutional neural network (CNN).
 Methods: Based on the Xiangya Medical Big Data P...

Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

American journal of clinical oncology
OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for ...

Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both...

General audio tagging with ensembling convolutional neural networks and statistical features.

The Journal of the Acoustical Society of America
Audio tagging aims to infer descriptive labels from audio clips and it is challenging due to the limited size of data and noisy labels. The solution to the tagging task is described in this paper. The main contributions include the following: an ense...

Computational neural network in melanocytic lesions diagnosis: artificial intelligence to improve diagnosis in dermatology?

European journal of dermatology : EJD
Diagnosis in dermatology is largely based on contextual factors going far beyond the visual and dermoscopic inspection of a lesion. Diagnostic tools such as the different types of dermoscopy, confocal microscopy and optical coherence tomography (OCT)...

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

JAMA dermatology
IMPORTANCE: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.