Papers Skin Cancer Classification
“Skin Cancer Classification” 태그가 달린 논문 56편 · 필터 해제
Transfer learning with class-weighted and focal loss function for automatic skin cancer classification
Skin cancer is by far in top-3 of the world's most common cancer. Among different skin cancer types, melanoma is particularly dangerous because of its ability to metastasize. Early detection is the key to success in skin…
Cancer ClassificationDeep LearningGeneral ClassificationSkin Cancer Classification+1Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classificati…
Cancer ClassificationData AugmentationSkin Cancer ClassificationConvolutional Neural Networks for Classifying Melanoma Images
In this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed leading to severe consequences including the death of patient. Also…
Cancer ClassificationSkin Cancer ClassificationTransfer LearningMelanoma Detection using Adversarial Training and Deep Transfer Learning
Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance o…
Conditional Image GenerationGeneral ClassificationImage GenerationImage-to-Image Translation+5Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages
Technology-assisted platforms provide reliable solutions in almost every field these days. One such important application in the medical field is the skin cancer classification in preliminary stages that need sensitive a…
Cancer ClassificationGeneral ClassificationRobust classificationSkin Cancer ClassificationAutomatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One s…
Cancer ClassificationGeneral ClassificationLesion DetectionLesion Segmentation+1Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models have achieved performance higher than t…
BIG-bench Machine LearningCancer ClassificationGeneral ClassificationSkin Cancer ClassificationData Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network
Deep Neural Networks (DNNs) show a significant impact on medical imaging. One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. T…
Cancer ClassificationData AugmentationGeneral ClassificationGenerative Adversarial Network+1A CNN toolbox for skin cancer classification
We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification. The implemented software architecture allows developers to quickly set up new convolutional neural…
AutoMLCancer ClassificationClassificationGeneral Classification+2Deep neural network or dermatologist?
Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A dow…
Cancer ClassificationDiagnosticSkin Cancer ClassificationSkin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet
Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is…
Cancer ClassificationDecision MakingDiagnosticGeneral Classification+2Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
In the last few years, Deep Learning (DL) has been showing superior performance in different modalities of biomedical image analysis. Several DL architectures have been proposed for classification, segmentation, and dete…
Cancer ClassificationClassificationGeneral ClassificationImage Segmentation+4Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automat…
Cancer ClassificationGeneral ClassificationMedical Image GenerationSkin Cancer ClassificationDermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152,…
Cancer ClassificationDiagnosticGeneral ClassificationSkin Cancer ClassificationData Augmentation for Skin Lesion Analysis
Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data aug…
Data AugmentationGeneral ClassificationSkin Cancer ClassificationSkin Lesion ClassificationKnowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a…
Deep Learningimage-classificationImage ClassificationMedical Image Analysis+2