GradPath: Task-Sensitive Pathway Tuning for Medical Image analysis

Parameter-efficient fine-tuning (PEFT) commonly adapts pretrained models either by inserting newly initialized modules at manually specified locations or by updating independently selected pretrained weights. The former introduces additional computation and placement sensitivity, while the latter overlooks the structural coupling within pretrained networks. We propose GradPath, a structured PEFT method that treats each hidden pathway as a paired adaptation unit consisting of an input-projection row and its corresponding output-projection column. GradPath estimates pathway importance using an activation–gradient sensitivity score and jointly updates the paired projections of the selected pathways. The selected pathways form a bottleneck-shaped subnetwork within the pretrained transformation, without introducing new projection matrices. We further develop GradPath-S, which enhances selected spatial pathways using gated multi-kernel depthwise convolutions. Extensive experiments on medical image classification, segmentation, and few-shot learning demonstrate strong performance across diverse modalities, architectures, and supervision levels. GradPath further reduces training memory, computation, and latency. These results establish structured pretrained pathways as an effective and efficient unit for parameter-efficient medical image adaptation.