Weakly Supervised Cell Instance Segmentation for Multi-Modality Microscopy

Ming Xue
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-8, 2023.

Abstract

Instance segmentation of multi-modality high-resolution microscopy images is an important task in computational pathology. We extended HoVer-Net[1], originally developed for segmentation and classification of nuclei in multi-Tissue histology images, to apply it under weakly supervised situation. According to the final tests, this modification also works for multi-modality microscopy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-xue23a, title = {Weakly Supervised Cell Instance Segmentation for Multi-Modality Microscopy}, author = {Xue, Ming}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--8}, year = {2023}, editor = {Ma, Jun and Xie, Ronald and Gupta, Anubha and Guilherme de Almeida, José and Bader, Gary D. and Wang, Bo}, volume = {212}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v212/xue23a/xue23a.pdf}, url = {https://proceedings.mlr.press/v212/xue23a.html}, abstract = {Instance segmentation of multi-modality high-resolution microscopy images is an important task in computational pathology. We extended HoVer-Net[1], originally developed for segmentation and classification of nuclei in multi-Tissue histology images, to apply it under weakly supervised situation. According to the final tests, this modification also works for multi-modality microscopy.} }
Endnote
%0 Conference Paper %T Weakly Supervised Cell Instance Segmentation for Multi-Modality Microscopy %A Ming Xue %B Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images %C Proceedings of Machine Learning Research %D 2023 %E Jun Ma %E Ronald Xie %E Anubha Gupta %E José Guilherme de Almeida %E Gary D. Bader %E Bo Wang %F pmlr-v212-xue23a %I PMLR %P 1--8 %U https://proceedings.mlr.press/v212/xue23a.html %V 212 %X Instance segmentation of multi-modality high-resolution microscopy images is an important task in computational pathology. We extended HoVer-Net[1], originally developed for segmentation and classification of nuclei in multi-Tissue histology images, to apply it under weakly supervised situation. According to the final tests, this modification also works for multi-modality microscopy.
APA
Xue, M.. (2023). Weakly Supervised Cell Instance Segmentation for Multi-Modality Microscopy. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-8 Available from https://proceedings.mlr.press/v212/xue23a.html.

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