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.
1from cervicalseg import CervicalSeg
23segmenter = CervicalSeg(device="auto")4result = segmenter.predict("image.jpg")56# result.mask contains only 0=background and 1=lesion.7result.save_mask("mask.png")# binary PNG containing 0 and 2558result.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.
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.
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。
