@inproceedings{d1c114676d0e4c999449150d92a63ea7,
title = "Apply Masked-attention Mask Transformer to Instance Segmentation in Pathology Images",
abstract = "Instance segmentation can be applied for the discrimination and diagnosis of cancer cells in pathology images. Accurate segmentation of each pathological cell in the pathology images can improve the efficiency of clinical diagnosis. In this paper, we aim to evaluate the state-of-the-art transformer-based instance segmentation method, masked-attention mask transformer (Mask2Former)[1], on pathology datasets. With the pretrained model of Mask2Former on the natural image instance segmentation dataset, we show that Mask2Former can be adaptive to small pathological datasets and achieve comparable or even better instance segmentation performance compared with the state-of-the-art task-specific pathology image instance segmentation methods.",
keywords = "cell segmentation, deep learning, instance segmentation, pathology images, transformer",
author = "Sheng, \{Jia Chun\} and Liao, \{Yi Sheng\} and Huang, \{Chun Rong\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Symposium on Computer, Consumer and Control, IS3C 2023 ; Conference date: 30-06-2023 Through 03-07-2023",
year = "2023",
doi = "10.1109/IS3C57901.2023.00098",
language = "English",
series = "Proceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "342--345",
booktitle = "Proceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023",
address = "美國",
}
