CervicalSeg

CervicalSeg is a four-class semantic 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.

宫颈阴道镜图像四分类语义分割模型，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。

Classes / 类别

Index	Class	Colour
0	background	black
1	blue	blue
2	green	green
3	red	red

Usage / 使用方法

pip install cervicalseg

Use cervicalseg>=0.1.1 for compatibility with both Transformers 4.56+ and 5.x DINOv3 module layouts.

请使用 cervicalseg>=0.1.1，以兼容 Transformers 4.56+ 与 5.x 的两种 DINOv3 模块命名。

from cervicalseg import CervicalSeg

segmenter = CervicalSeg(device="auto")
result = segmenter.predict("image.jpg")
result.save_mask("mask.png")
result.save_color_mask("color_mask.png")
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.

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。

Evaluation / 评估结果

The data were split at the patient level to prevent patient leakage between train, validation, and test sets.

数据按患者划分，避免同一患者同时出现在训练、验证或测试集合中。

Split	Samples	Foreground mIoU	Foreground Dice	Pixel accuracy
Validation	221	0.4075	0.5704	0.9121
Test	216	0.3633	0.5296	0.8891

Test-set per-class metrics:

Class	Precision	Recall	Dice	IoU
blue	0.5651	0.5697	0.5674	0.3961
green	0.3784	0.4950	0.4289	0.2730
red	0.6850	0.5219	0.5924	0.4209

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.

• Performance may not generalize to devices, institutions, populations, or acquisition conditions not represented in the training data.

• Green-region segmentation is the weakest class in the reported test results.

• 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。

• 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)