Version 1
: Received: 31 July 2024 / Approved: 1 August 2024 / Online: 1 August 2024 (12:04:04 CEST)
Version 2
: Received: 20 August 2024 / Approved: 21 August 2024 / Online: 21 August 2024 (18:21:10 CEST)
How to cite:
Gao, X.; Li, H.; You, C.; Zhou, L.; Shi, Q.; Yang, Z.; Shao, S.; Zhang, Y. Explainable Artificial Intelligence (XAI) for Diagnosing and Treating Tumors of the Female Reproductive Systems: Challenges and Advances. Preprints2024, 2024080047. https://doi.org/10.20944/preprints202408.0047.v2
Gao, X.; Li, H.; You, C.; Zhou, L.; Shi, Q.; Yang, Z.; Shao, S.; Zhang, Y. Explainable Artificial Intelligence (XAI) for Diagnosing and Treating Tumors of the Female Reproductive Systems: Challenges and Advances. Preprints 2024, 2024080047. https://doi.org/10.20944/preprints202408.0047.v2
Gao, X.; Li, H.; You, C.; Zhou, L.; Shi, Q.; Yang, Z.; Shao, S.; Zhang, Y. Explainable Artificial Intelligence (XAI) for Diagnosing and Treating Tumors of the Female Reproductive Systems: Challenges and Advances. Preprints2024, 2024080047. https://doi.org/10.20944/preprints202408.0047.v2
APA Style
Gao, X., Li, H., You, C., Zhou, L., Shi, Q., Yang, Z., Shao, S., & Zhang, Y. (2024). Explainable Artificial Intelligence (XAI) for Diagnosing and Treating Tumors of the Female Reproductive Systems: Challenges and Advances. Preprints. https://doi.org/10.20944/preprints202408.0047.v2
Chicago/Turabian Style
Gao, X., Sicong Shao and Yudong Zhang. 2024 "Explainable Artificial Intelligence (XAI) for Diagnosing and Treating Tumors of the Female Reproductive Systems: Challenges and Advances" Preprints. https://doi.org/10.20944/preprints202408.0047.v2
Abstract
Background: Ovarian, cervical, and endometrial cancers stand as fatal health killers for women's mental and physical health, especially affecting female reproductive systems. Exploiting weakly supervised learning and explainable AI techniques are crucial for fast, accurate, and robust automatic marker detection, efficient prevention, and primary treatment of gynecological tumors. Methods: With respect to the PRIMSA methodology, weakly supervised learning schemes and deep learning-based schemes are investigated in the cross-subject fields of clinical diagnostic imaging and explainable AI technology. Related methods, opening research problems, and challenging subjects are explored in the screening and treatment of gynecological tumors and performing cancer image diagnosis in the latest study. Results: Keynote approaches combining ultrasound medicine, explainable AI, and clinical imaging technology are summarized. In combination with learning-based methods with medical imaging, prospective insights are put forward for refreshed concepts on treatments in the area of gynecological oncology, with a feasible range of applications for their corresponding AI techniques. Conclusions: Explainable AI and deep learning-based approaches are capable of performing accurate classification between benign and malignant tumors, yielding a pathway to care for women's health, improving the survival rate of patients, pacing with disease prediction, and matching the strategic goal of "Healthy China 2030".
Keywords
Artificial intelligence (AI); gynecological oncology; women’s health; weakly supervised learning; deep learning; diagnosis and treatment
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.