AIMC Topic: Skin Neoplasms

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Robustness of convolutional neural networks in recognition of pigmented skin lesions.

European journal of cancer (Oxford, England : 1990) Jan 7, 2021
BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin c...
Reproducibility of Results Neural Networks, Computer Diagnosis, Computer-Assisted Melanoma Diagnosis, Differential Skin Neoplasms Predictive Value of Tests Dermoscopy Humans Image Interpretation, Computer-Assisted Nevus
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Universal adversarial attacks on deep neural networks for medical image classification.

BMC medical imaging Jan 7, 2021
BACKGROUND: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-...
Diagnostic Imaging Tomography, Optical Coherence Image Interpretation, Computer-Assisted Radiography, Thoracic Neural Networks, Computer Skin Neoplasms Pneumonia Photography Humans Diabetic Retinopathy
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Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BMC medical imaging Jan 6, 2021
BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able ...
Dermoscopy Deep Learning ROC Curve Humans Image Interpretation, Computer-Assisted Melanoma Skin Neoplasms
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Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Cancer research Dec 21, 2020
Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists...
Melanoma Skin Neoplasms Reproducibility of Results Sensitivity and Specificity Algorithms Prognosis Female Lymphocytes, Tumor-Infiltrating Predictive Value of Tests Melanoma, Cutaneous Malignant Image Interpretation, Computer-Assisted Medical Oncology Breast Neoplasms Datasets as Topic Biomedical Research Neoplasms Humans Image Processing, Computer-Assisted Machine Learning Software
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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...
Diagnosis, Differential Skin Image Interpretation, Computer-Assisted Datasets as Topic Nevus Deep Learning Adolescent Child, Preschool Child Male Middle Aged Infant Humans Retrospective Studies Aged Female Melanoma Aged, 80 and over Infant, Newborn Skin Neoplasms Young Adult Dermoscopy Adult Reproducibility of Results
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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...
Carcinoma, Squamous Cell Humans Diagnosis, Computer-Assisted Algorithms Deep Learning Skin Neoplasms
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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...
Dermatologists Melanoma Skin Neoplasms Humans Deep Learning Artificial Intelligence
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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...
Skin Progression-Free Survival Drug Resistance, Neoplasm Humans Machine Learning Immune Checkpoint Inhibitors Prospective Studies Risk Assessment Image Processing, Computer-Assisted Disease Progression Aged Prognosis Follow-Up Studies ROC Curve Female Neoplasm Staging Adult Melanoma Male Skin Neoplasms Middle Aged
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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 Neural Networks, Computer Machine Learning Algorithms Skin Neoplasms Carcinoma, Basal Cell Skin 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...
Melanoma Skin Neoplasms Deep Learning Dermoscopy Humans Skin Diseases
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