Abstract
Examining pathology images through visual microscopy is widely considered the most reliable method for diagnosing different medical conditions. Although deep learning-based methods show great potential for aiding pathology image analysis, they are hindered by the lack of accessible large-scale annotated data. Large text-to-image models have significantly advanced the synthesis of diverse contexts within natural image analysis, thereby expanding existing datasets. However, the variety of histomorphological features in pathology images, which differ from that of natural images, has been less explored. In this paper, we propose a histomorphology-focused pathology image synthesis (HistoSyn) method. Specifically, HistoSyn constructs instructive textural prompts from spatial and morphological attributes of pathology images. It involves analyzing the intricate patterns and structures found within pathological images and translating these visual details into descriptive prompts. Furthermore, HistoSyn presents new criteria for image quality evaluation focusing on spatial and morphological characteristics which have a stronger correlation to the performance of down-stream tasks. Experiments have demonstrated that our method can achieve a diverse range of high-quality pathology images, with a focus on histomorphological attributes. The code is available at https://github.com/7LFB/HistoSyn.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 |
Subtitle of host publication | 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part IV |
Editors | Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel |
Publisher | Springer Cham |
Pages | 200-210 |
Number of pages | 11 |
Edition | 1st |
ISBN (Electronic) | 9783031720833 |
ISBN (Print) | 9783031720826 |
DOIs | |
Publication status | Published - 13 Oct 2024 |
Event | 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco Duration: 6 Oct 2024 → 10 Oct 2024 https://link.springer.com/book/10.1007/978-3-031-72083-3 (Conference Proceedings (Part IV)) https://link.springer.com/book/10.1007/978-3-031-72111-3 (Conference Proceedings (Part VIII)) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 15004 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention |
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Conference
Conference | 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 |
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Country/Territory | Morocco |
City | Marrakesh |
Period | 6/10/24 → 10/10/24 |
Internet address |
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Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science
User-Defined Keywords
- Histomorphology features
- Pathology image synthesis