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I would like to ask you a question, I pre-train on my medical image data set (6k) according to the official pre-training code, and then the downstream task is segmentation, but the fine-tuning results…
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no. we didn't optimize it during fine-tuning.
_Originally posted by @JunMa11 in https://github.com/bowang-lab/MedSAM/issues/291#issuecomment-2286929055_
Hello, I woul…
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We've successfully utilized the SAM 2 framework to address both 2D and 3D medical image segmentation tasks.
Feel free to check **paper** [**Medical SAM 2**](https://arxiv.org/abs/2408.00874) here 🤩
…
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Here are useful ressources to look into:
- Our lab metrics definition: https://github.com/ivadomed/MetricsReloaded
- Panoptica: [Panoptica -- instance-wise evaluation of 3D semantic and instance se…
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I am currently pursuing my Master's degree in medical image segmentation via deep learning based method. I recently read your paper titled "SLoRD: Structural Low-Rank Descriptors for Shape Consisten…
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The demo url has a bad performance in medical image segmentation, how could I fine-tune it? could you give some prompt or code?
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**Submitting author:** @nathandecaux (Nathan Decaux)
**Repository:** https://github.com/nathandecaux/labelprop
**Branch with paper.md** (empty if default branch):
**Version:** v1.2
**Editor:** @likea…
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** Environment **
PyTorch information
-------------------
PyTorch version: 1.13.1+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04…
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Hi, thanks for all the work done!
Is it possible to leverage just the SAM-Med3D encoder (with sam_med3d_turbo) for inference without click prompts? I want to use its encoder with a custom decoder i…