Gao, X.; Li, H.; You, C.; Zhou, L.; Shi, Q.; Yang, Z.; Shao, S.; Zhang, Y. Challenges and Advances on Explainable Artificial Intelligence (AI): Diagnosing and Treating Tumors of the Female Reproductive Systems. Preprints2024, 2024080047. https://doi.org/10.20944/preprints202408.0047.v1
APA Style
Gao, X., Li, H., You, C., Zhou, L., Shi, Q., Yang, Z., Shao, S., & Zhang, Y. (2024). Challenges and Advances on Explainable Artificial Intelligence (AI): Diagnosing and Treating Tumors of the Female Reproductive Systems. Preprints. https://doi.org/10.20944/preprints202408.0047.v1
Chicago/Turabian Style
Gao, X., Sicong Shao and Yudong Zhang. 2024 "Challenges and Advances on Explainable Artificial Intelligence (AI): Diagnosing and Treating Tumors of the Female Reproductive Systems" Preprints. https://doi.org/10.20944/preprints202408.0047.v1
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: Weakly supervised learning 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 on the screening and treatment of gynecological tumors and performing cancer image diagnosis in the latest study. Results: Keynote approaches combining ultrasound medicine, AI, and clinical imaging technology are summarized. Combining learning-based schemes 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.
