Chaos-Enhanced Prototypical Networks for Few-Shot Medical Image Classification
The scarcity of labeled clinical data in oncology makes Few-Shot Learning (FSL) a critical framework for Computer Aided Diagnostics, but we observed that standard Prototypical Networks often struggle with the "prototype instability" caused by morphological noise and high intra-class variance in brain tumor scans. Our work attempts to minimize this by integrating a non-linear Logistic Chaos Module into a fine-tuned ResNet-18 backbone creating the Chaos-Enhanced ProtoNet(CE-ProtoNet). Using the deterministic ergodicity of the logistic chaos map we inject controlled perturbations into support features during episodic training-essentially for "stress testing" the embedding space. This process...
| Authors | Chinthakuntla Meghan Sai, Murarisetty V Sai Kartheek, Sita Devi Bharatula et al. |
|---|---|
| Primary category | eess.IV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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