Zero-Training Task-Specific Model Synthesis for Few-Shot Medical Image Classification
This addresses the bottleneck of data scarcity in medical AI, enabling rapid deployment for rare diseases, though it is a new paradigm rather than incremental.
The paper tackles the problem of few-shot medical image classification by proposing Zero-Training Task-Specific Model Synthesis (ZS-TMS), which uses a generative engine to synthesize classifier parameters from minimal inputs like a single image and text description, achieving state-of-the-art results on benchmarks such as ISIC 2018 and a rare disease dataset, especially in 1-shot and 5-shot regimes.
Deep learning models have achieved remarkable success in medical image analysis but are fundamentally constrained by the requirement for large-scale, meticulously annotated datasets. This dependency on "big data" is a critical bottleneck in the medical domain, where patient data is inherently difficult to acquire and expert annotation is expensive, particularly for rare diseases where samples are scarce by definition. To overcome this fundamental challenge, we propose a novel paradigm: Zero-Training Task-Specific Model Synthesis (ZS-TMS). Instead of adapting a pre-existing model or training a new one, our approach leverages a large-scale, pre-trained generative engine to directly synthesize the entire set of parameters for a task-specific classifier. Our framework, the Semantic-Guided Parameter Synthesizer (SGPS), takes as input minimal, multi-modal task information as little as a single example image (1-shot) and a corresponding clinical text description to directly synthesize the entire set of parameters for a task-specific classifier. The generative engine interprets these inputs to generate the weights for a lightweight, efficient classifier (e.g., an EfficientNet-V2), which can be deployed for inference immediately without any task-specific training or fine-tuning. We conduct extensive evaluations on challenging few-shot classification benchmarks derived from the ISIC 2018 skin lesion dataset and a custom rare disease dataset. Our results demonstrate that SGPS establishes a new state-of-the-art, significantly outperforming advanced few-shot and zero-shot learning methods, especially in the ultra-low data regimes of 1-shot and 5-shot classification. This work paves the way for the rapid development and deployment of AI-powered diagnostic tools, particularly for the long tail of rare diseases where data is critically limited.