---
license: cc-by-nc-nd-4.0
library_name: pytorch
tags:
  - medical-imaging
  - 3d-cnn
  - ultrasound
  - focused-ultrasound
  - transcranial-ultrasound
  - reproduction
  - fine-tuned
datasets:
  - vinkle-srivastav/TFUScapes
base_model: masonwang025/deeptfus-base
language:
  - en
---

# DeepTFUS: variant C (soft-argmax cranked + anchor restored)

*A reproduction attempt of DeepTFUS, proposed by [Srivastav et al. (arXiv:2505.12998)](https://arxiv.org/abs/2505.12998).*

Fine-tune of [`masonwang025/deeptfus-base`](https://huggingface.co/masonwang025/deeptfus-base)
that's a single-knob counterfactual to variant B: same aggressive
soft-argmax recipe, but with the paper's `gradient_L1=0.1` anchor
restored. Tests whether the anchor matters at this aggression level.

⭐ Statistically tied with B on the three paper headline metrics; the
anchor exists to suppress secondary hot-spots (median secondary-lobe
volume drops 99% vs B), not to move `focal_mm`.

## Modification (vs base)

```
loss.focal_weight       0   to 5e-5     (+soft-argmax term)
loss.focal_temperature  :   to 0.03     (sharper than A's 0.05)
loss.grad_weight        0.1 to 0.1      (anchor KEPT, single knob diff vs B)
```

3-epoch warmup ramp on `focal_weight`. Fine-tune ran 10 epochs from base
ckpt at `lr=3e-5`; shipped ckpt is `ckpt_epoch_008.pt` (best
val_focal_mm in plateau).

## Test results (n = 597)

| metric | paper | base | C (this model) | Δ vs base | Δ vs B |
|---|---:|---:|---:|---:|---:|
| `relative_l2` mean ± std | 0.414 ± 0.086 | 0.384 ± 0.078 | **0.388 ± 0.077** | +0.005 (in budget) | 0.000 (tied) |
| `relative_l2` median | 0.394 | 0.369 | 0.372 | +0.003 | 0.000 |
| `focal_position_error_mm` mean ± std | 2.89 ± 2.14 | 6.49 ± 4.58 | 5.11 ± 3.76 | −1.38 mm | +0.05 (≈ tied) |
| `focal_position_error_mm` median | 2.45 | 5.15 | **4.15** | **−1.00 mm (−19%)** | −0.03 (≈ tied) |
| `max_pressure_error` mean ± std | 0.199 ± 0.158 | 0.225 ± 0.116 | 0.239 ± 0.106 | +0.014 | 0.000 |
| `max_pressure_error` median | 0.166 | 0.217 | 0.239 | +0.022 | 0.000 |
| `focal_pressure_error` median | : | 0.528 | 0.496 | −0.032 | −0.006 |
| `focal_iou_fwhm` median | : | 0.143 | 0.136 | −0.007 | 0.000 |
| `inference_latency_s` median | : | 0.233 | 0.232 | unchanged | unchanged |

## Other variants and discussion

See the [Collection](https://huggingface.co/collections/masonwang025/deeptfus-reproduction-6a03e39286a09470b960511f)
for the other 5 variants, 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-ft-c-softargmax-anchored", "ckpt_best.pt"),
    map_location="cpu", weights_only=False,
)
```

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.