CrossCT: CNN and Transformer cross–teaching for multimodal image cell segmentation

Sara Joubbi, Giorgio Ciano, Dario Cardamone, Giuseppe Maccari, Duccio Medini
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-14, 2023.

Abstract

Segmenting microscopy images is a crucial step for quantitatively analyzing biological imaging data. Classical tools for biological image segmentation need to be adjusted to the cell type and image conditions to get decent results. Another limitation is the lack of high-quality labeled data to train alternative methods like Deep Learning since manual labeling is costly and time-consuming. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was organized by NeurIPS to solve this problem. The aim of the challenge was to develop a versatile method that can work with high variability, with few labeled images, a lot of unlabeled images, and with no human interaction. We developed CrossCT, a framework based on the cross–teaching between a CNN and a Transformer. The main idea behind this work was to improve the organizers’ baseline methods and use both labeled and unlabeled data. Experiments show that our method outperforms the baseline methods based on a supervised learning approach. We achieved an F1 score of 0.5988 for the Transformer and 0.5626 for the CNN respecting the time limits imposed for inference.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-joubbi23a, title = {CrossCT: CNN and Transformer cross–teaching for multimodal image cell segmentation}, author = {Joubbi, Sara and Ciano, Giorgio and Cardamone, Dario and Maccari, Giuseppe and Medini, Duccio}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--14}, 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/joubbi23a/joubbi23a.pdf}, url = {https://proceedings.mlr.press/v212/joubbi23a.html}, abstract = {Segmenting microscopy images is a crucial step for quantitatively analyzing biological imaging data. Classical tools for biological image segmentation need to be adjusted to the cell type and image conditions to get decent results. Another limitation is the lack of high-quality labeled data to train alternative methods like Deep Learning since manual labeling is costly and time-consuming. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was organized by NeurIPS to solve this problem. The aim of the challenge was to develop a versatile method that can work with high variability, with few labeled images, a lot of unlabeled images, and with no human interaction. We developed CrossCT, a framework based on the cross–teaching between a CNN and a Transformer. The main idea behind this work was to improve the organizers’ baseline methods and use both labeled and unlabeled data. Experiments show that our method outperforms the baseline methods based on a supervised learning approach. We achieved an F1 score of 0.5988 for the Transformer and 0.5626 for the CNN respecting the time limits imposed for inference.} }
Endnote
%0 Conference Paper %T CrossCT: CNN and Transformer cross–teaching for multimodal image cell segmentation %A Sara Joubbi %A Giorgio Ciano %A Dario Cardamone %A Giuseppe Maccari %A Duccio Medini %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-joubbi23a %I PMLR %P 1--14 %U https://proceedings.mlr.press/v212/joubbi23a.html %V 212 %X Segmenting microscopy images is a crucial step for quantitatively analyzing biological imaging data. Classical tools for biological image segmentation need to be adjusted to the cell type and image conditions to get decent results. Another limitation is the lack of high-quality labeled data to train alternative methods like Deep Learning since manual labeling is costly and time-consuming. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was organized by NeurIPS to solve this problem. The aim of the challenge was to develop a versatile method that can work with high variability, with few labeled images, a lot of unlabeled images, and with no human interaction. We developed CrossCT, a framework based on the cross–teaching between a CNN and a Transformer. The main idea behind this work was to improve the organizers’ baseline methods and use both labeled and unlabeled data. Experiments show that our method outperforms the baseline methods based on a supervised learning approach. We achieved an F1 score of 0.5988 for the Transformer and 0.5626 for the CNN respecting the time limits imposed for inference.
APA
Joubbi, S., Ciano, G., Cardamone, D., Maccari, G. & Medini, D.. (2023). CrossCT: CNN and Transformer cross–teaching for multimodal image cell segmentation. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-14 Available from https://proceedings.mlr.press/v212/joubbi23a.html.

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