WBC Semantic Segmentation Model

• A Mask2Former model is trained on the training split of the WBCAtt+ dataset
• Training code is at ./m2f_tiny_1024_color_20260414_091312.
• The model weight is uploaded to HuggingFace
• Put model_epoch=050.ckpt in the directory ./m2f_tiny_1024_color_20260414_091312.
• See ./results_sample_in_the_wild.ipynb to learn how to use.

Environment

• The commands used to make the environment:

conda create -n wbcsegmentor python=3.12.13
conda activate wbcsegmentor
pip install uv
uv pip install torch==2.10.0 torchvision --index-url https://download.pytorch.org/whl/cu128
uv pip install ipykernel
python -m ipykernel install --user --name wbcsegmentor 
uv pip install transformers==5.5.0
uv pip install requests
uv pip install matplotlib pandas tqdm pillow scikit-learn seaborn numpy scipy opencv-python ipywidgets gitpython nvitop gpustat scikit-image pyyaml

• See environment.yml.

Evaluation on WBCAtt+ dataset

• See ./results_pbc_eval.ipynb for more details and more examples.

Class Index	Class Name	IoU (%)
0	(Background)	(99.32)
1	Cytoplasm	96.67
2	Nucleus	98.14
3	Platelets	92.91
4	RedBloodCell	99.43
5	Vacuoles	81.20

Mean IoU (mIoU) without Background class: 93.67

Sample Results in the Wild

• See ./results_sample_in_the_wild.ipynb.

Reference*

*The model trained in this repository is not used in the paper. The Mask2Former here is trained using Transformers library, while the one reported in the paper is based on Mask2Former's official codebase.

If you find this code or pretrained model useful, please consider citing:

• Satoshi Tsutsui, Winnie Pang, Shuting He, and Bihan Wen, “WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images,” Medical Image Analysis, 2026.
• ArXiv: http://arxiv.org/abs/2605.19692

@article{tsutsui2026wbcattplus,
  title={WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images},
  author={Tsutsui, Satoshi and Pang, Winnie and He, Shuting and Wen, Bihan},
  journal={Medical Image Analysis},
  year={2026}
}