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- High level API to define cell/nuclei instance segmentation models.
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-4 cell/nuclei instance segmentation models and more to come.
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-5 cell/nuclei instance segmentation models and more to come.
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- Open source datasets for training and benchmarking.
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- Pre-trained backbones/encoders from the [timm](https://github.com/rwightman/pytorch-image-models) library.
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- Pre-trained backbones/encoders from the [timm](https://github.com/huggingface/pytorch-image-models) library.
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- All the architectures can be augmented to **panoptic segmentation**.
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- A lot of flexibility to modify the components of the model architectures.
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- Sliding window inference for large images.
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- Multi-GPU inference.
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- Popular training losses and benchmarking metrics.
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- Simple model training with [pytorch-lightning](https://www.pytorchlightning.ai/).
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- Benchmarking utilities both for model latency & segmentation performance.
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- Regularization techniques to tackle batch effects/domain shifts.
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- Ability to add transformers to the decoder layers.
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- Example notebooks to train models with [lightning](https://lightning.ai/docs/pytorch/latest/) or [accelerate](https://huggingface.co/docs/accelerate/index).
-[Training Stardist with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_stardist.ipynb). Train the Stardist model with constant sized Pannuke patches.
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-[Training Cellpose with Lizard](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/lizard_nuclei_segmentation_cellpose.ipynb). Train the Cellpose model with Lizard dataset that is composed of varying sized images.
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-[Training Hover-Net with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_hovernet.ipynb). Here we train the Hover-Net nuclei segmentation model with an `imagenet` pretrained `resnet50` backbone from the `timm` library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained by utilizing [lightning](https://lightning.ai/docs/pytorch/latest/) (with checkpointing).
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-[Training Stardist with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_stardist.ipynb). Here we train the Stardist multi-class nuclei segmentation model with an `imagenet` pretrained `efficientnetv2_s` backbone from the `timm` library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained by utilizing [lightning](https://lightning.ai/docs/pytorch/latest/).
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-[Training CellPose with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_cellpose.ipynb). Here we train the CellPose multi-class nuclei segmentation model with an `imagenet` pretrained `convnext_small` backbone from the `timm` library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing [accelerate](https://huggingface.co/docs/accelerate/index) by hugginface.
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-[Training OmniPose with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_omnipose.ipynb). Here we train the OmniPose multi-class nuclei segmentation model with an `imagenet` pretrained `focalnet_small_lrf` backbone from the `timm` library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing [accelerate](https://huggingface.co/docs/accelerate/index) by hugginface.
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-[Finetuning CellVit-SAM with Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_nuclei_segmentation_cellvit.ipynb). Here we finetune the CellVit-SAM multi-class nuclei segmentation model with a `SA-1B` pretrained SAM-image-encoder backbone. The encoder is transformer based `VitDet`-model. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing [accelerate](https://huggingface.co/docs/accelerate/index) by hugginface.
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-[Training CellPose with Lizard](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/lizard_nuclei_segmentation_cellpose.ipynb). Train the Cellpose model with Lizard dataset that is composed of varying sized images.
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-[Benchmarking Cellpose Trained on Pannuke](https://github.com/okunator/cellseg_models.pytorch/blob/main/examples/pannuke_cellpose_benchmark.ipynb). Benchmark Cellpose trained on Pannuke. Both the model performance and latency.
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## Code Examples
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-[1] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019.
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-[2] Stringer, C.; Wang, T.; Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation Nature Methods, 2021, 18, 100-106
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-[3] Cutler, K. J., Stringer, C., Wiggins, P. A., & Mougous, J. D. (2022). Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. bioRxiv. doi:10.1101/2021.11.03.467199
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-[4] Uwe Schmidt, Martin Weigert, Coleman Broaddus, & Gene Myers (2018). Cell Detection with Star-Convex Polygons. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II (pp. 265–273).
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-[5] Gamper, J., Koohbanani, N., Benet, K., Khuram, A., & Rajpoot, N. (2019) PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In European Congress on Digital Pathology (pp. 11-19).
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-[6] Gamper, J., Koohbanani, N., Graham, S., Jahanifar, M., Khurram, S., Azam, A.,Hewitt, K., & Rajpoot, N. (2020). PanNuke Dataset Extension, Insights and Baselines. arXiv preprint arXiv:2003.10778.
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-[7] Graham, S., Jahanifar, M., Azam, A., Nimir, M., Tsang, Y.W., Dodd, K., Hero, E., Sahota, H., Tank, A., Benes, K., & others (2021). Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 684-693).
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-[5] Hörst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J., Grünwald, B., Egger, J., & Kleesiek, J. (2023). CellViT: Vision Transformers for Precise Cell Segmentation and Classification (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2306.15350.
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-[6] Gamper, J., Koohbanani, N., Benet, K., Khuram, A., & Rajpoot, N. (2019) PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In European Congress on Digital Pathology (pp. 11-19).
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-[7] Gamper, J., Koohbanani, N., Graham, S., Jahanifar, M., Khurram, S., Azam, A.,Hewitt, K., & Rajpoot, N. (2020). PanNuke Dataset Extension, Insights and Baselines. arXiv preprint arXiv:2003.10778.
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-[8] Graham, S., Jahanifar, M., Azam, A., Nimir, M., Tsang, Y.W., Dodd, K., Hero, E., Sahota, H., Tank, A., Benes, K., & others (2021). Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 684-693).
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