Simplify model card with concise usage and MedMLX branding

README.md

@@ -17,38 +17,35 @@ pipeline_tag: image-segmentation
 base_model: MONAI/brats_mri_segmentation
 ---
 
-# SegResNet Brain Tumor Segmentation
+<img src="https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/banner.png" alt="MedMLX — Segment. BRAIN MRI / SEGRESNET" width="100%">
 
-Native MLX port of [MONAI BraTS MRI segmentation](https://huggingface.co/MONAI/brats_mri_segmentation).
-Segment brain tumor regions from four MRI sequences locally on Apple Silicon.
+# SegResNet · Brain Tumor
 
-- T1, contrast-enhanced T1, T2 and FLAIR inputs
-- Native whole-volume segmentation with MLX
-- Converted MONAI SegResNet checkpoint included
+Segment brain tumor regions from four MRI sequences with the MONAI SegResNet checkpoint.
 
-MLX implementation by [MedMLX](https://github.com/MedMLX).
-[GitHub](https://github.com/MedMLX/segresnet-mlx) · [Usage](https://github.com/MedMLX/segresnet-mlx#quick-start) · [Upstream](https://huggingface.co/MONAI/brats_mri_segmentation)
+[GitHub](https://github.com/MedMLX/segresnet-mlx) · [Files](https://huggingface.co/MedMLX/segresnet-brats-mlx/tree/main) · [Docs](https://github.com/MedMLX/segresnet-mlx/blob/main/README.md#quick-start)
 
-**Requires the companion `segresnet-mlx` runtime.** Follow the quick start below.
+| Input | Output |
+| :--- | :--- |
+| T1, T1ce, T2 + FLAIR · NIfTI | Brain tumor segmentation |
 
-## Requirements
-
-Apple Silicon Mac with Metal GPU access, Python 3.13 or 3.14 and [uv](https://docs.astral.sh/uv/).
-The weights are included here; checkpoint conversion is not needed.
+**Apple Silicon · Metal · Python 3.13 / 3.14**
 
 ## Quick start
 
 ```bash
 uv venv --python 3.13
 source .venv/bin/activate
-uv pip install "segresnet-mlx @ https://github.com/MedMLX/segresnet-mlx/releases/download/v0.2.1/segresnet_mlx-0.2.1-py3-none-any.whl"
+
+repo="https://github.com/MedMLX/segresnet-mlx"
+release="$repo/releases/download/v0.2.1"
+uv pip install "$release/segresnet_mlx-0.2.1-py3-none-any.whl"
 uv tool install huggingface-hub
 
 hf download MedMLX/segresnet-brats-mlx --local-dir models
 ```
 
-Authenticate with `hf auth login` if repository access is required.
-Run this example in the same environment, replacing the input paths with your own:
+Replace the example input paths with your own files.
 
 ```python
 from segresnet_mlx.medmlx import run
@@ -65,65 +62,10 @@ result = run(
 print(result["outputs"])
 ```
 
-## Input and output
-
-Input is four 3D NIfTI MRI volumes on the same voxel grid: T1,
-contrast-enhanced T1, T2 and FLAIR. The example names each modality explicitly.
-
-Output is `brats_mri_segmentation_mask.nii.gz` on the input grid. Exported
-labels use the bundle’s BraTS convention: 0 background, 1 tumor core,
-2 whole tumor outside the core, 4 enhancing tumor.
-
-## Technical details
-
-The companion runtime is [v0.2.1](https://github.com/MedMLX/segresnet-mlx/releases/tag/v0.2.1), source `b5cc265cd0426fe8f64b40a8ed9be4c42a599724`, with `medmlx-core==0.2.3`. All 83 tensors here exactly match a fresh CPU host conversion of the pinned BraTS source checkpoint through that installed release's public `convert_state_dict` API. The weights were retained unchanged.
-
-The current fresh-wheel suite passed 145 tests with zero skips: 133 ordinary cases and 12 released **renal** checkpoint cases. Those renal results do not qualify this BraTS checkpoint. The full-volume BraTS comparison below is historical and retains its original software revision and one-case scope; no new full-volume BraTS or clinical performance claim is made.
-
-Historical native result: **success**. Original-code comparison: **pass**, `minimum_per_label_dice` = `1.0`.
-
-Native whole-volume output compared with the original MONAI bundle; the October 12 renal operator repair also has a full-case BraTS regression.
-
-These are recorded results for one configuration, not a new benchmark or an
-estimate of clinical accuracy. The [native run](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/native-run.json)
-and [original-code comparison](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/reference-comparison.json)
-retain the commands, precision, geometry and output checks.
-
-<details>
-<summary>Checkpoint provenance and recorded configuration</summary>
-
-Recorded configuration: **MONAI brats_mri_segmentation (SegResNet, 4-channel MRI)**.
-
-Evaluated package revision: `40cbce54523632fbc8cdfc564a9cce48d8df3424`.
-
-Recorded input: MSD Task01 BrainTumour (training) / BRATS_001.
-
-Original publisher: MONAI.
-Original implementation: [upstream source](https://huggingface.co/MONAI/brats_mri_segmentation).
-Source revision or version: `370f7f9d062745fbac445e7fe6d6616d35df04ec`.
-Original checkpoint: [pinned checkpoint](https://huggingface.co/MONAI/brats_mri_segmentation/resolve/370f7f9d062745fbac445e7fe6d6616d35df04ec/models/model.pt).
-
-Weights were converted with the companion MLX package, without retraining.
-Conversion changes tensor layout or storage as required by that implementation.
-
-The [replay harness](https://github.com/MedMLX/Med-MLX/blob/main/benchmarks/README.md)
-describes how to reproduce the recorded configuration with local paths.
-
-</details>
-
-## Intended use
-
-Research and education. Outputs require human review and are not intended for
-clinical diagnosis or treatment decisions.
-
-## License
+All four MRI volumes must share the same voxel grid.
 
-Weights: **Apache-2.0** ([license](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/LICENSE)).
-Upstream code: **Apache-2.0**. MLX port code:
-[Apache-2.0](https://github.com/MedMLX/segresnet-mlx/blob/main/LICENSE).
+## Use & license
 
-Retain the [attribution and notices](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/NOTICE) when redistributing
-the bundle or a conversion:
+For research and education. Not validated for clinical use.
 
-- Retain the applicable license, copyright and attribution notices; identify the upstream checkpoint, authors, and MLX conversion changes.
-- Include Apache-2.0, retain applicable upstream NOTICE content, and mark modifications.
+Weights: [Apache-2.0](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/LICENSE). [Notices](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/NOTICE).