Skin Lesion Classification
36 papers with code • 1 benchmarks • 7 datasets
HAM10000
MSK
BCN_20000
SD-198
Latest papers with no code
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model.
Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models
The proposed model increased classification accuracy by 3% (from 94. 2% to 98. 18%) in comparison with other methods.
Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification
Firstly, unlike current methods that usually employ two individual models for for clinical and dermoscopy modalities, we verified that multimodal feature can be learned by sharing the parameters of encoder while leaving the individual modal-specific classifiers.
Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks
In the realm of skin lesion image classification, the intricate spatial and semantic features pose significant challenges for conventional Convolutional Neural Network (CNN)-based methodologies.
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration
The rising global prevalence of skin conditions, some of which can escalate to life-threatening stages if not timely diagnosed and treated, presents a significant healthcare challenge.
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification
The objective of this paper is to study Conformal Prediction, an emerging distribution-free uncertainty quantification technique, and provide a comprehensive understanding of the advantages and limitations inherent in various methods within the medical imaging field.
Multi-task Explainable Skin Lesion Classification
The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
However, small or heavily imbalanced datasets lead to a reduced conformity of the results due to the influence of these factors on the methods performance.
Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification
An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneous melanoma by differentiating between highly suspicious and benign lesions.
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification
To evaluate the performance of our proposed approach, we conduct experiments on two publicly available medical image classification benchmark datasets: the skin lesion classification (ISIC 2018) and the blood cell classification dataset (BCCD).
