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One model is all you need : multi-task learning enables simultaneous histology image segmentation and classification
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Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Raza, Shan E. Ahmed, Minhas, Fayyaz ul Amir Afsar, Snead, David and Rajpoot, Nasir M. (Nasir Mahmood) (2023) One model is all you need : multi-task learning enables simultaneous histology image segmentation and classification. Medical Image Analysis, 83 . 102685. doi:10.1016/j.media.2022.102685 ISSN 13618415.
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Official URL: https://doi.org/10.1016/j.media.2022.102685
Abstract
The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600 thousand objects for segmentation and 440 thousand patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900 thousand and 2.1 million nuclei, glands and lumina respectively. We make this resource available to remove a major barrier in the development of explainable models for computational pathology.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Journal or Publication Title: | Medical Image Analysis | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 13618415 | ||||||||
| Official Date: | January 2023 | ||||||||
| Dates: |
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| Volume: | 83 | ||||||||
| Article Number: | 102685 | ||||||||
| DOI: | 10.1016/j.media.2022.102685 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 7 December 2022 | ||||||||
| Date of first compliant Open Access: | 7 December 2022 | ||||||||
| Related URLs: |
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