Papers Skin Cancer Classification
“Skin Cancer Classification” 태그가 달린 논문 56편 · 필터 해제
SpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging
Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliability and robustness concerns. Models may exploit spurious correlati…
Skin Cancer ClassificationContrastive LearningSearching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification
Background/Objectives: Dermoscopic skin-lesion classifiers lose accuracy when images arrive from a new clinic or a new device. We asked which data augmentations reduce that loss, and measured the effect under a protocol …
Skin Cancer ClassificationDomain GeneralizationData AugmentationGeometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification
Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Ex…
Skin Lesion ClassificationSkin Cancer ClassificationMultimodal ReasoningGraph LearningMedicalRec: Medical recommender system for image classification without retraining
The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring s…
Medical Image ClassificationSkin Cancer ClassificationA Generative AI Approach for Reducing Skin Tone Bias in Skin Cancer Classification
Skin cancer is one of the most common cancers worldwide and early detection is critical for effective treatment. However, current AI diagnostic tools are often trained on datasets dominated by lighter skin tones, leading…
Skin Cancer ClassificationBinary ClassificationLesion SegmentationData AugmentationImpact of domain adaptation in deep learning for medical image classifications
Domain adaptation (DA) is a quickly expanding area in machine learning that involves adjusting a model trained in one domain to perform well in another domain. While there have been notable progressions, the fundamental …
Skin Cancer ClassificationClassifier calibrationFederated LearningDomain AdaptationSkin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks
Skin cancer can be identified by dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), using annotated skin images and Convolutional Ne…
Skin Lesion ClassificationSkin Cancer ClassificationImage AugmentationTransfer LearningTowards Explainable Skin Cancer Classification: A Dual-Network Attention Model with Lesion Segmentation and Clinical Metadata Fusion
Skin cancer is a life-threatening disease where early detection significantly improves patient outcomes. Automated diagnosis from dermoscopic images is challenging due to high intra-class variability and subtle inter-cla…
Skin Lesion ClassificationSkin Cancer ClassificationLesion SegmentationSkin Lesion Classification Based on ResNet-50 Enhanced With Adaptive Spatial Feature Fusion
Skin cancer classification is challenging due to high inter-class similarity, intra-class variability, and artifacts in dermoscopic images. To address these issues, we propose an improved ResNet-50 with Adaptive Spatial …
Skin Lesion ClassificationSkin Cancer ClassificationSkin Cancer Classification: Hybrid CNN-Transformer Models with KAN-Based Fusion
Skin cancer classification is a crucial task in medical image analysis, where precise differentiation between malignant and non-malignant lesions is essential for early diagnosis and treatment. In this study, we explore …
Medical Image ClassificationSkin Cancer ClassificationRepresentation LearningTransfer LearningComparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
The emergence of Vision Transformers (ViTs) has revolutionized computer vision, yet their effectiveness compared to traditional Convolutional Neural Networks (CNNs) in medical imaging remains under-explored. This study p…
Medical Image ClassificationSkin Cancer ClassificationBrain Tumor ClassificationPneumonia DetectionUncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation
Accurate and reliable skin cancer diagnosis is critical for early treatment and improved patient outcomes. Deep learning (DL) models have shown promise in automating skin cancer classification, but their performance can …
Cancer ClassificationLesion ClassificationMedical DiagnosisSkin Cancer Classification+4Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off
The rapid advancement of deep learning in medical image analysis has greatly enhanced the accuracy of skin cancer classification. However, current state-of-the-art models, especially those based on transfer learning like…
Cancer ClassificationMedical Image AnalysisSkin Cancer ClassificationTransfer LearningUsing Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial fo…
Cancer ClassificationDecision MakingDeep LearningSkin Cancer ClassificationCancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin Lesion Dataset for Skin Cancer Classification
In the United States, skin cancer ranks as the most commonly diagnosed cancer, presenting a significant public health issue due to its high rates of occurrence and the risk of serious complications if not caught early. R…
Cancer ClassificationSkin Cancer ClassificationLeveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency
Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective …
Cancer ClassificationComputational EfficiencyData AugmentationKnowledge Distillation+2A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be v…
Cancer ClassificationSkin Cancer ClassificationSkin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System
Skin cancer is a global health concern, necessitating early and accurate diagnosis for improved patient outcomes. This study introduces a groundbreaking approach to skin cancer classification, employing the Vision Transf…
Cancer ClassificationDeep LearningSkin Cancer ClassificationSkin Cancer SegmentationJoint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification
Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images. Although good classification results have been shown, more accurate results can …
Cancer ClassificationClassificationDecision MakingMulti-modal Classification+1Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinct…
Cancer ClassificationDecision MakingSkin Cancer Classification