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Authors: Amruta Parulekar 1 ; Utkarsh Kanwat 1 ; Ravi Gupta 1 ; Medha Chippa 1 ; Thomas Jacob 1 ; Tripti Bameta 2 ; Swapnil Rane 2 and Amit Sethi 1

Affiliations: 1 Indian Institute of Technology, Bombay, Mumbai, India ; 2 Tata Memorial Centre-ACTREC (HBNI), Mumbai, India

Keyword(s): Cell Nuclei, Classification, Histopathology, Segmentation.

Abstract: Segmentation and classification of cell nuclei using deep neural networks (DNNs) can save pathologists’ time for diagnosing various diseases, including cancers. The accuracy of DNNs increases with the sizes of annotated datasets available for training. The available public datasets with nuclear annotations and labels differ in their class label sets. We propose a method to train DNNs on multiple datasets where the set of classes across the datasets are related but not the same. Our method is designed to utilize class hierarchies, where the set of classes in a dataset can be at any level of the hierarchy. Our results demonstrate that segmentation and classification metrics for the class set used by the test split of a dataset can improve by pre-training on another dataset that may even have a different set of classes due to the expansion of the training set enabled by our method. Furthermore, generalization to previously unseen datasets also improves by combining multiple other datase ts with different sets of classes for training. The improvement is both qualitative and quantitative. The proposed method can be adapted for various loss functions, DNN architectures, and application domains. (More)

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Paper citation in several formats:
Parulekar, A.; Kanwat, U.; Gupta, R.; Chippa, M.; Jacob, T.; Bameta, T.; Rane, S. and Sethi, A. (2024). Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 281-288. DOI: 10.5220/0012380800003657

@conference{bioimaging24,
author={Amruta Parulekar. and Utkarsh Kanwat. and Ravi Gupta. and Medha Chippa. and Thomas Jacob. and Tripti Bameta. and Swapnil Rane. and Amit Sethi.},
title={Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING},
year={2024},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012380800003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING
TI - Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
SN - 978-989-758-688-0
IS - 2184-4305
AU - Parulekar, A.
AU - Kanwat, U.
AU - Gupta, R.
AU - Chippa, M.
AU - Jacob, T.
AU - Bameta, T.
AU - Rane, S.
AU - Sethi, A.
PY - 2024
SP - 281
EP - 288
DO - 10.5220/0012380800003657
PB - SciTePress

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