Keshav0781/OCT-SAM-FineTuning-LoRA

This project is a standard application of Parameter-Efficient Fine-Tuning (PEFT) techniques to a popular foundation model (SAM) for a specific medical niche (OCT). Quantitatively, with 0 stars and forks over 200 days, it lacks any community traction or validation. From a competitive standpoint, it is a tutorial-level implementation of well-documented patterns. It faces significant displacement risk from established medical foundation models like MedSAM, which is trained on millions of medical images, and newer iterations like SAM 2 that offer superior zero-shot performance. The moat is non-existent as the project does not provide a proprietary dataset, a clinical-grade pipeline, or a novel architecture. Platform risk is high because healthcare cloud providers (AWS, Google Cloud) are increasingly offering pre-trained medical segmentation endpoints that negate the need for custom, lightweight fine-tuning scripts like this one.