AI RESEARCH
Attention-Based Chaotic Self-Supervision for Medical Image Classification
arXiv CS.CV
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ArXi:2605.04985v1 Announce Type: new Deep learning models for medical image classification usually achieve promising results but typically rely on large, annotated datasets or standard transfer learning from ImageNet. Self-Supervised Learning (SSL) has emerged as a powerful alternative, yet common methods like masked autoencoders (MAEs) may inadvertently destroy fine-grained diagnostic features by using random masking. In this paper, we propose a novel SSL pre-