Long-Tailed Classification

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Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions
May 4, 2026Jiaxiang Jiang, Mahesh Subedar, Omesh TickooSkin Lesion Classification Synthetic Data Augmentation
Intel · Google
Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification
May 4, 2026Duy Hoang Khuong, Duy Nguyen Huu, Ngu Huynh Cong VietClass-Imbalanced Learning Long-Tailed Classification
Department of Artificial Intelligence FPT University Ho Chi Minh City, Vietnam · Department of Computing Fundamental FPT University Ho Chi Minh City, Vietnam
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
May 4, 2026Guanmeng Xian, Ning Yang, Philip S. YuAdversarial Training Long-Tailed Classification
Sichuan University, Chengdu, China · University of Illinois at Chicago, USA
Decision Boundary-aware Generation for Long-tailed Learning
May 2, 2026Jiacheng Yang, Ruichi Zhang, Chikai Shang +5Synthetic Data Augmentation Long-Tailed Classification
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China · College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China · Department of Statistical Science, University College London, London, UK +1
CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning
May 2, 2026Ruichi Zhang, Chikai Shang, Jiacheng Yang +4Long-Tailed Classification Vision Foundation Model Adaptation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China · College of Computer Science and Software Engineering, Shenzhen University, China · Institute of High Performance Computing, A*STAR, Singapore
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Apr 16, 2026Hexin Dong, Yi Lin, Pengyu Zhou +25Zero-Shot Learning Long-Tailed Classification
Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
Apr 2, 2026Zhanliang Wang, Hongzhuo Chen, Quan Minh Nguyen +2Long-Tailed Classification Learning to Rank
University of Pennsylvania, Philadelphia, PA, USA · Children’s Hospital of Philadelphia, Philadelphia, PA, USA

May 4, 2026, cs.CVJ/K move · Enter open · S save

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

Jiaxiang Jiang, Mahesh Subedar, Omesh Tickoo

Intel · Google

Skin Lesion Classification Synthetic Data Augmentation Long-Tailed Classification Medical Image Classification Diffusion-Based Image Generation

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Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically underperform on tail classes with limited samples. This limitation is particularly problematic in medical applications, where rare classes often correspond to severe or high-risk diseases and therefore require high diagnostic accuracy. Existing solutions-including specialized architectures, rebalanced loss functions, and handcrafted data augmentation-offer only marginal improvements and struggle to scale due to their limited and largely deterministic variability. To address these challenges, we introduce a diffusion-model-driven synthetic data augmentation pipeline tailored for medical long-tailed classification. Our approach features a novel inpainting diffusion model combined with an Out-of-Distribution (OOD) post-selection mechanism to ensure diverse, realistic, and clinically meaningful synthetic samples. Evaluated on the ISIC2019 skin lesion classification dataset, one of the largest and most imbalanced medical imaging benchmarks, our method yields substantial improvements in overall performance, with particularly pronounced gains on tail classes with more than

28\%

improvement on the class with the fewest samples. These results demonstrate the effectiveness of diffusion-based augmentation in mitigating long-tail imbalance and enhancing medical classification robustness.

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