TY - GEN
T1 - Semi-supervised Semantic Segmentation for Effusion Cytology Images
AU - Aboobacker, Shajahan
AU - Vijayasenan, Deepu
AU - David, S. Sumam
AU - Suresh, Pooja K.
AU - Sreeram, Saraswathy
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Cytopathologists analyse images captured at different magnifications to detect the malignancies in effusions. They identify the malignant cell clusters from the lower magnification, and the identified area is zoomed in to study cell level details in high magnification. The automatic segmentation of low magnification images saves scanning time and storage requirements. This work predicts the malignancy in the effusion cytology images at low magnification levels such as 10 × and 4 ×. However, the biggest challenge is the difficulty in annotating the low magnification images, especially the 4 × data. We extend a semi-supervised learning (SSL) semantic model to train unlabelled 4 × data with the labelled 10 × data. The benign F-score on the predictions of 4 × data using the SSL model is improved 15% compared with the predictions of 4 × data on the semantic 10 × model.
AB - Cytopathologists analyse images captured at different magnifications to detect the malignancies in effusions. They identify the malignant cell clusters from the lower magnification, and the identified area is zoomed in to study cell level details in high magnification. The automatic segmentation of low magnification images saves scanning time and storage requirements. This work predicts the malignancy in the effusion cytology images at low magnification levels such as 10 × and 4 ×. However, the biggest challenge is the difficulty in annotating the low magnification images, especially the 4 × data. We extend a semi-supervised learning (SSL) semantic model to train unlabelled 4 × data with the labelled 10 × data. The benign F-score on the predictions of 4 × data using the SSL model is improved 15% compared with the predictions of 4 × data on the semantic 10 × model.
UR - https://www.scopus.com/pages/publications/85161589931
UR - https://www.scopus.com/inward/citedby.url?scp=85161589931&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-7867-8_34
DO - 10.1007/978-981-19-7867-8_34
M3 - Conference contribution
AN - SCOPUS:85161589931
SN - 9789811978661
T3 - Lecture Notes in Networks and Systems
SP - 429
EP - 440
BT - Computer Vision and Machine Intelligence - Proceedings of CVMI 2022
A2 - Tistarelli, Massimo
A2 - Dubey, Shiv Ram
A2 - Singh, Satish Kumar
A2 - Jiang, Xiaoyi
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Computer Vision and Machine Intelligence, CVMI 2022
Y2 - 12 August 2022 through 13 August 2022
ER -