LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification

• URL: http://arxiv.org/abs/2212.12741v2
• Date: Fri, 6 Sep 2024 13:49:30 GMT
• Title: LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification
• Authors: Abu Adnan Sadi, Labib Chowdhury, Nusrat Jahan, Mohammad Newaz Sharif Rafi, Radeya Chowdhury, Faisal Ahamed Khan, Nabeel Mohammed,
• Abstract summary: We propose a novel framework called Large Margin aware (LMF) loss to mitigate the class imbalance problem in medical imaging datasets. This framework harnesses the distinct characteristics of both loss functions by enforcing wider margins for minority classes. We provide empirical evidence that our proposed framework consistently outperforms other baseline methods.
• Score: 2.4866930218890837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With advances in digital technology, the classification of medical images has become a crucial step for image-based clinical decision support systems. Automatic medical image classification represents a pivotal domain where the use of AI holds the potential to create a significant social impact. However, several challenges act as obstacles to the development of practical and effective solutions. One of these challenges is the prevalent class imbalance problem in most medical imaging datasets. As a result, existing AI techniques, particularly deep-learning-based methodologies, often underperform in such scenarios. In this study, we propose a novel framework called Large Margin aware Focal (LMF) loss to mitigate the class imbalance problem in medical imaging. The LMF loss represents a linear combination of two loss functions optimized by two hyperparameters. This framework harnesses the distinct characteristics of both loss functions by enforcing wider margins for minority classes while simultaneously emphasizing challenging samples found in the datasets. We perform rigorous experiments on three neural network architectures and with four medical imaging datasets. We provide empirical evidence that our proposed framework consistently outperforms other baseline methods, showing an improvement of 2%-9% in macro-f1 scores. Through class-wise analysis of f1 scores, we also demonstrate how the proposed framework can significantly improve performance for minority classes. The results of our experiments show that our proposed framework can perform consistently well across different architectures and datasets. Overall, our study demonstrates a simple and effective approach to addressing the class imbalance problem in medical imaging datasets. We hope our work will inspire new research toward a more generalized approach to medical image classification.

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