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For my use case, segmentation is all about 3D segmentation of volumes in medical imaging. SAM 2 was tried, mostly using a 2D slice approach, but I don't think i
by SubiculumCode 11mo ago
For my use case, segmentation is all about 3D segmentation of volumes in medical imaging. SAM 2 was tried, mostly using a 2D slice approach, but I don't think it was competitive with the current gold standard nn-unet[1]
[1. https://github.com/MIC-DKFZ/nnUNet https://github.com/MIC-DKFZ/nnUNet]
- visioninmyblood 11mo agoAgreed that Unet has been the most used model for medical imaging for the last 10 years since the initial Unet paper. I think a combination of Llm+VLMs could be a way forward for medical imaging. I tried it out here and it works great. https://chat.vlm.run/c/e062aa6d-41bb-4fc2-b3e4-7e70b45562cf https://chat.vlm.run/c/e062aa6d-41bb-4fc2-b3e4-7e70b45562cf
- davycro 11mo agoSame. My use case is ultrasound segmentation. These models struggle, understandably so, with medical imaging.
- aDyslecticCrow 11mo agoU-net is a brilliant architecture, and it still seems to beat this model in scaling up the segmentation mask from 256x256 back to the real image. I also don't think unet really benefits from the massive internal feature size given by a the visual transformer used for image encoding. But I'm impressed by the ability of this model to create a image encoding that is independent of the prompt. I feel like there may be lessons in training approach that can be carried over to unet for a more valuable encoding.