File size: 2,919 Bytes
4bee0a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | ---
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.
|