  The application of large-scale models in medical image segmentation demands
substantial quantities of meticulously annotated data curated by experts along
with high computational resources, both of which are challenges in
resource-poor settings. In this study, we present the Medical Segment Anything
Model with Galore MedSAGa where we adopt the Segment Anything Model (SAM) to
achieve memory-efficient, few-shot medical image segmentation by applying
Gradient Low-Rank Projection GaLore to the parameters of the image encoder of
SAM. Meanwhile, the weights of the prompt encoder and mask decoder undergo full
parameter fine-tuning using standard optimizers. We further assess MedSAGa's
few-shot learning capabilities, reporting on its memory efficiency and
segmentation performance across multiple standard medical image segmentation
datasets. We compare it with several baseline models, including LoRA fine-tuned
SAM (SAMed) and DAE-Former. Experiments across multiple datasets and these
baseline models with different number of images for fine tuning demonstrated
that the GPU memory consumption of MedSAGa is significantly less than that of
the baseline models, achieving an average memory efficiency of 66% more than
current state-of-the-art (SOTA) models for medical image segmentation. The
combination of substantially lower memory requirements and comparable to SOTA
results in few-shot learning for medical image segmentation positions MedSAGa
as an optimal solution for deployment in resource-constrained settings.
