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t1-mri-denoising
yushangjinghong/t1-mri-denoising
t1-mri-denoising is a machine learning model from yushangjinghong. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
Author: 雷钧翔 | Student ID: 23307140057
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Updated Jun 20, 2026
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.pt2.7 GB · 100%
At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 20, 2026
- Updated
- Jun 20, 2026
- SHA
- ed1458bf
Model card
From the Hugging Face model README
T1 MRI Denoising — 2.5D Residual U-Net with SE Attention
Author: 雷钧翔 | Student ID: 23307140057
2.5D Residual U-Net for T1 MRI denoising (noisy T1 → clean T1), with Squeeze-and-Excitation channel attention and composite loss (L1 + SSIM + Gradient).
Models
| File | Params | PSNR | SSIM |
|---|---|---|---|
small/best.pt | 31M | 44.99 dB | 0.9907 |
large/best.pt | 193.7M | 45.14 dB | 0.9908 |
Noisy baseline: PSNR = 43.34 dB, SSIM = 0.9664
Architecture
- Input: 5 adjacent axial slices stacked as channels (2.5D), 192×192
- Backbone: Residual U-Net with InstanceNorm + SiLU activations
- Attention: SE (Squeeze-and-Excitation) after each ConvBlock
- Output: Residual prediction →
pred_clean = clamp(noisy_center + residual, 0, 1)
Training
- Loss:
L1 + 0.1×(1−SSIM) + 0.05×GradLoss - SSIM: 11×11 Gaussian kernel (σ=1.5)
- Optimizer: AdamW (lr=2e-4, wd=1e-4)
- Scheduler: CosineAnnealingLR (η_min=1e-6)
- AMP fp16 + gradient clipping (max_norm=1.0)
Usage
import torch
checkpoint = torch.load("large/best.pt", map_location="cpu")
# checkpoint contains: model_state_dict, args, epoch, best_val_psnr
See GitHub repo for full training code.
- Library
- pytorch
- License
- mit
- Created
- Jun 20, 2026
- Updated
- Jun 20, 2026