Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging

Carolin Teuber, Anwai Archit, Constantin Pape

Keywords: segment anything, peft, biomedical image segmentation

TL;DR: This paper highlights the application of PEFT methods to efficiently fine-tune vision foundation models like SAM, MicroSAM and MedicoSAM for better biomedical image segmentation, reducing computational costs and improving performance on diverse data.

Abstract: Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation methods, but challenges remain in generalization to new conditions, requiring costly data annotation. Vision foundation models, such as Segment Anything Model (SAM), address this issue through improved generalization. However, these models still require finetuning on annotated data, although with less annotations, to achieve optimal results for new conditions. As a downside, they require more computational resources. This makes parameter-efficient finetuning (PEFT) relevant. We contribute the first comprehensive study of PEFT for SAM applied to biomedical images. We find that the placement of PEFT layers is more important for efficiency than the type of layer for vision transformers and we provide a recipe for resource-efficient finetuning.

Primary Subject Area: Foundation Models

Secondary Subject Area: Segmentation

Paper Type: Methodological Development

Registration Requirement: Yes

Reproducibility: https://github.com/computational-cell-analytics/peft-sam

Visa & Travel: Yes

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Submission Number: 184