| --- |
| license: other |
| license_name: dinov3-license |
| pipeline_tag: image-segmentation |
| library_name: cervicalseg |
| tags: |
| - medical-imaging |
| - cervical |
| - binary-segmentation |
| - dinov3 |
| - dpt |
| - wtconv |
| --- |
| |
| # CervicalSeg |
|
|
| CervicalSeg is a binary lesion segmentation model for cervical colposcopy images. It combines a |
| DINOv3 ViT-S/16 backbone, a DPT decoder, and WTConv in the final two high-resolution decoder fusion |
| stages. It predicts the lesion region only and does not classify red, blue, or green annotation |
| categories. |
|
|
| CervicalSeg 是宫颈阴道镜图像二分类病灶分割模型，采用 DINOv3 ViT-S/16、DPT 解码器，并在最后 |
| 两个高分辨率融合阶段使用 WTConv。模型只预测病灶区域，不再区分红、蓝、绿标注类别。 |
|
|
| ## Classes / 类别 |
|
|
| | Index | Class | Meaning | |
| |---:|---|---| |
| | 0 | background | non-lesion region / 非病灶区域 | |
| | 1 | lesion | lesion region / 病灶区域 | |
|
|
| ## Usage / 使用方法 |
|
|
| ```bash |
| pip install "cervicalseg>=0.2.0" |
| ``` |
|
|
| ```python |
| from cervicalseg import CervicalSeg |
| |
| segmenter = CervicalSeg(device="auto") |
| result = segmenter.predict("image.jpg") |
| |
| # result.mask contains only 0=background and 1=lesion. |
| result.save_mask("mask.png") # binary PNG containing 0 and 255 |
| result.save_overlay("overlay.jpg") |
| ``` |
|
|
| The first call downloads `model.safetensors`; later calls reuse the Hugging Face cache. Output masks |
| are restored to the original image size. The single overlay colour is for visualization only and is |
| not a lesion subtype. |
|
|
| 首次调用会下载 `model.safetensors`，后续调用复用 Hugging Face 缓存。输出 mask 会恢复到原图尺寸。 |
| 叠加图中的单一颜色仅用于显示病灶范围，不代表病灶类别。 |
|
|
| ## Training and evaluation / 训练与评估 |
|
|
| The public v0.2.0 checkpoint was trained for 51 epochs on all 1,465 available images: 1,028 from the |
| original training split, 221 from validation, and 216 from test. All non-background annotations were |
| merged into one lesion label. Because all samples were used to fit the final model, no held-out test |
| metric applies to this public checkpoint. |
|
|
| 公开的 v0.2.0 权重使用全部 1,465 张图像训练 51 个 epoch，其中原 train/val/test 分别为 |
| 1,028/221/216 张。所有非背景标注合并为一个病灶标签。由于最终模型使用了全部样本训练，因此该 |
| 公开权重没有独立测试集指标。 |
|
|
| For model-development reference only, the separate patient-level validation experiment at epoch 51 |
| obtained lesion IoU 0.5847 and lesion Dice 0.7379. These are development results, not an independent |
| evaluation of the all-data public checkpoint. |
|
|
| 仅供模型开发参考：按患者划分的独立验证实验在第 51 个 epoch 得到病灶 IoU 0.5847、病灶 Dice |
| 0.7379；这些结果不是对全量训练公开权重的独立测试。 |
|
|
| ## Intended use and limitations / 用途与限制 |
|
|
| - Research use only. |
| - Not a medical device and not for clinical diagnosis or treatment decisions. |
| - Predictions require review by qualified professionals. |
| - The model detects lesion extent but does not predict lesion grade, pathology, or colour category. |
| - Performance may not generalize to devices, institutions, populations, or acquisition conditions |
| not represented in the training data. |
|
|
| - 仅用于研究。 |
| - 不是医疗器械，不得直接用于临床诊断或治疗决策。 |
| - 预测结果需要由具备资质的专业人员复核。 |
| - 模型仅检测病灶范围，不预测病灶分级、病理结果或颜色类别。 |
| - 对训练数据未覆盖的设备、机构、人群和采集条件，模型性能可能下降。 |
|
|
| ## License / 许可证 |
|
|
| The CervicalSeg Python wrapper is released under the MIT License. The trained weights contain |
| DINOv3 materials and are distributed under the DINOv3 License included as `DINOV3_LICENSE.md`. |
| WTConv-derived code retains its MIT notice in `WTCONV_LICENSE`. |
|
|
| CervicalSeg Python 封装代码采用 MIT License。训练权重包含 DINOv3 材料，权重分发遵循仓库中的 |
| `DINOV3_LICENSE.md`。WTConv 衍生代码保留其 MIT 许可声明。 |
|
|
| ## Author |
|
|
| Shi Minghai (`PlanetSMH`) |
|
|
