AIMC Topic: Skin Neoplasms

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

Journal of dermatological science Dec 2, 2020
BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate...
Infant Skin Neoplasms Deep Learning Adolescent Adult Male Middle Aged Skin Image Interpretation, Computer-Assisted Female Datasets as Topic Infant, Newborn Nevus Dermoscopy Melanoma Child, Preschool Reproducibility of Results Humans Child Retrospective Studies Diagnosis, Differential Aged Aged, 80 and over Young Adult
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Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

Medical image analysis Nov 21, 2020
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 constit...
Diagnosis, Computer-Assisted Deep Learning Algorithms Skin Neoplasms Humans Carcinoma, Squamous Cell
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Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.

The Journal of dermatology Nov 19, 2020
Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties f...
Artificial Intelligence Deep Learning Humans Melanoma Dermatologists Skin Neoplasms
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Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research Nov 18, 2020
PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI respon...
Progression-Free Survival Drug Resistance, Neoplasm Follow-Up Studies Neoplasm Staging Skin Middle Aged Risk Assessment Disease Progression ROC Curve Immune Checkpoint Inhibitors Image Processing, Computer-Assisted Humans Aged Machine Learning Female Adult Prospective Studies Melanoma Prognosis Male Skin Neoplasms
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Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Nov 13, 2020
Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin t...
Humans Skin Neoplasms Carcinoma, Basal Cell Skin Neural Networks, Computer Machine Learning Algorithms Pathologists
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Deep learning based classification of facial dermatological disorders.

Computers in biology and medicine Nov 13, 2020
Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognit...
Humans Melanoma Skin Neoplasms Dermoscopy Deep Learning Skin Diseases
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Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

Computers in biology and medicine Oct 27, 2020
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population,...
Skin Neoplasms Dermoscopy Humans Diagnosis, Computer-Assisted Artificial Intelligence Algorithms
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Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

The Journal of dermatology Oct 15, 2020
In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant archives is helpful when applied for patients with darker skin. This study compared the performance ...
Skin Neoplasms Humans Dermatologists Dermoscopy Japan Neural Networks, Computer Deep Learning
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Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Sensors (Basel, Switzerland) Oct 13, 2020
Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim...
Humans Deep Learning Melanoma Neural Networks, Computer Skin Neoplasms Algorithms Dermoscopy
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Performance of a deep learning-based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists.

International journal of dermatology Oct 11, 2020
Dermatologists Dermatology Skin Neoplasms Carcinoma, Basal Cell Humans Deep Learning
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