Energy-Balance-Based Out-of-Distribution Detection of Skin Lesions

Skin lesion detection plays a crucial role in the diagnosis and treatment of skin diseases. Due to the wide variety of skin lesion types, especially when dealing with unknown or rare lesions, models tend to exhibit overconfidence. Out-of-distribution (OOD) detection techniques are capable of identifying lesion types that were not present in the training data, thereby enhancing the model's robustness and diagnostic reliability. However, the issue of class imbalance makes it difficult for models to effectively learn the features of minority class lesions. To address this challenge, a Balanced Energy Regularization Loss is proposed in this paper, aimed at mitigating the class imbalance problem in OOD detection. This method applies stronger regularization to majority class samples, promoting the model's learning of minority class samples, which significantly improves model performance. Experimental results demonstrate that the Balanced Energy Regularization Loss effectively enhances the model's robustness and accuracy in OOD detection tasks, providing a viable solution to the class imbalance issue in skin lesion detection.

@article{Sun2025,
  title     = {Energy-Balance-Based Out-of-Distribution Detection of Skin Lesions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jiahui Sun and Guan Yang and Yishuo Chen and Hongyan Wu and Xiaoming Liu},
  doi       = {10.14569/IJACSA.2025.0160255},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160255}
}