Papers Skin Cancer Classification
“Skin Cancer Classification” 태그가 달린 논문 56편 · 필터 해제
Revisiting Skin Tone Fairness in Dermatological Lesion Classification
Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones. However, the absence of skin tone labels in public datasets hinders bu…
Cancer ClassificationClassificationFairnessLesion Classification+1SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification
Skin cancer is a treatable disease if discovered early. We provide a production-specific solution to the skin cancer classification problem that matches human performance in melanoma identification by training a vision t…
Cancer ClassificationClassificationCPUGPU+4Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classif…
Cancer ClassificationDomain AdaptationSkin Cancer ClassificationUnsupervised Domain AdaptationMulti-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network
Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesion…
Cancer ClassificationData AugmentationDeep LearningSkin Cancer Classification+1CIFF-Net: Contextual Image Feature Fusion for Melanoma Diagnosis
Melanoma is considered to be the deadliest variant of skin cancer causing around 75\% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrentl…
Melanoma DiagnosisSkin Cancer ClassificationAdversarial Attacks and Defences for Skin Cancer Classification
There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentati…
Adversarial AttackAdversarial DefenseCancer ClassificationClassification+5Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released…
Cancer ClassificationSkin Cancer ClassificationLeveraging Contextual Data Augmentation for Generalizable Melanoma Detection
While skin cancer detection has been a valuable deep learning application for years, its evaluation has often neglected the context in which testing images are assessed. Traditional melanoma classifiers assume that their…
AttributeData AugmentationSkin Cancer ClassificationReversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Reliable skin cancer diagnosis models play an essential role in early screening and medical intervention. Prevailing computer-aided skin cancer classification systems employ deep learning approaches. However, recent stud…
Cancer ClassificationDenoisingSkin Cancer ClassificationComparison of Deep Learning and Machine Learning Models and Frameworks for Skin Lesion Classification
The incidence rate for skin cancer has been steadily increasing throughout the world, leading to it being a serious issue. Diagnosis at an early stage has the potential to drastically reduce the harm caused by the diseas…
Cancer ClassificationLesion ClassificationLesion DetectionSkin Cancer Classification+1New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor
Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic ski…
Cancer ClassificationQuantizationSkin Cancer ClassificationSkin Cancer Classification using Inception Network and Transfer Learning
Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 trainin…
Cancer ClassificationClassificationSkin Cancer ClassificationTransfer LearningBenchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset
Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classific…
BenchmarkingCancer ClassificationDeep LearningSkin Cancer Classification+1Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an …
Skin Cancer ClassificationSoft-Attention Improves Skin Cancer Classification Performance
In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effecti…
Cancer ClassificationClassificationGeneral ClassificationImage Classification+2A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
Over the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase …
Cancer ClassificationPrognosisSkin Cancer ClassificationEnabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical image analysis, a well-designed augment…
Cancer ClassificationData AugmentationDiversityMedical Image Analysis+1Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets
Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable train…
Skin Cancer ClassificationTransfer LearningSemi-Supervised Federated Peer Learning for Skin Lesion Classification
Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recen…
Cancer ClassificationClassificationFederated LearningGeneral Classification+3An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification
Computer-aided skin cancer classification systems built with deep neural networks usually yield predictions based only on images of skin lesions. Despite presenting promising results, it is possible to achieve higher per…
Cancer ClassificationClassificationSkin Cancer ClassificationSkin Lesion Classification