| --- |
| license: cc-by-nc-nd-4.0 |
| library_name: pytorch |
| tags: |
| - medical-imaging |
| - 3d-cnn |
| - ultrasound |
| - focused-ultrasound |
| - transcranial-ultrasound |
| - reproduction |
| datasets: |
| - vinkle-srivastav/TFUScapes |
| language: |
| - en |
| --- |
| |
| # DeepTFUS: base (run-1 reproduction) |
|
|
| *A reproduction attempt of DeepTFUS, proposed by [Srivastav et al. (arXiv:2505.12998)](https://arxiv.org/abs/2505.12998).* |
|
|
| This is the from-scratch baseline: 50 epochs on the paper recipe |
| (weighted-MSE + λ·gradient-L1, no focal-position aux), `base_width=16` |
| (3.4 M params), `pure-bf16`, `batch=4` at 256³ resolution. Given a 3D |
| head CT and a transducer placement, predicts the resulting in-skull |
| pressure field in <1 s on an H100 (≈ 50× faster than the k-Wave |
| physics simulator the dataset was generated from). |
|
|
| ⭐ Partial reproduction: matched paper on `relative_l2`, did not match |
| on `focal_position_error_mm` (~2× worse) or `max_pressure_error`. This |
| gap motivated the 5 fine-tune variants in this model collection. |
|
|
| ## Test results (n = 597 held-out CT × placement combinations) |
|
|
| | metric | paper | base (this model) | reproduced? | |
| |---|---:|---:|---| |
| | `relative_l2` mean ± std | 0.414 ± 0.086 | **0.384 ± 0.078** | ✅ Yes (slightly beats paper) | |
| | `relative_l2` median | 0.394 | **0.369** | ✅ | |
| | `focal_position_error_mm` mean ± std | 2.89 ± 2.14 | 6.49 ± 4.58 | ❌ No (~2.25× worse mean) | |
| | `focal_position_error_mm` median | 2.45 | 5.15 | ❌ | |
| | `max_pressure_error` mean ± std | 0.199 ± 0.158 | 0.225 ± 0.116 | ✅ Yes (within paper's std) | |
| | `max_pressure_error` median | 0.166 | 0.217 | (slightly above paper) | |
| | `focal_pressure_error` median | : | 0.528 | : | |
| | `focal_iou_fwhm` median | : | 0.143 | : | |
| | `inference_latency_s` (b=1, H100) | 11.4 (RTX 4090) | 0.233 | 49× faster (different HW) | |
|
|
| ## Other variants and discussion |
|
|
| See the [Collection](https://huggingface.co/collections/masonwang025/deeptfus-reproduction-6a03e39286a09470b960511f) |
| for the 5 fine-tune variants built from this base ckpt, and the |
| [project page](https://masonjwang.com/projects/reproducing-deeptfus) |
| for the full reproduction story, interactive viewer, and discussion of |
| trade-offs. |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import torch |
| |
| ckpt = torch.load( |
| hf_hub_download("masonwang025/deeptfus-base", "ckpt_best.pt"), |
| map_location="cpu", weights_only=False, |
| ) |
| # ckpt['model'] : state_dict for the model defined in masonwang025/deeptfus repo |
| # ckpt['config'] : training config (architecture knobs + train hyperparams) |
| # ckpt['epoch'] : 43 (best by val_rel_l2) |
| ``` |
|
|
| Model code: [github.com/masonwang025/deeptfus](https://github.com/masonwang025/deeptfus). |
|
|
| ## Citation & License |
|
|
| Paper: Srivastav et al., [arXiv:2505.12998](https://arxiv.org/abs/2505.12998), 2025. |
|
|
| License: [CC-BY-NC-ND-4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/), |
| matching the TFUScapes dataset license. |
|
|
