Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation

• URL: http://arxiv.org/abs/2409.03087v1
• Date: Wed, 4 Sep 2024 21:22:54 GMT
• Title: Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation
• Authors: Amir Syahmi, Xiangrong Lu, Yinxuan Li, Haoxuan Yao, Hanjun Jiang, Ishita Acharya, Shiyi Wang, Yang Nan, Xiaodan Xing, Guang Yang,
• Abstract summary: We introduce a robust and versatile framework that combines AI and crowdsourcing to improve the quality and quantity of medical image datasets. Our approach utilise a user-friendly online platform that enables a diverse group of crowd annotators to label medical images efficiently. We employ pix2pixGAN, a generative AI model, to expand the training dataset with synthetic images that capture realistic morphological features.
• Score: 3.7274206780843477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in medical imaging and artificial intelligence (AI) have greatly enhanced diagnostic capabilities, but the development of effective deep learning (DL) models is still constrained by the lack of high-quality annotated datasets. The traditional manual annotation process by medical experts is time- and resource-intensive, limiting the scalability of these datasets. In this work, we introduce a robust and versatile framework that combines AI and crowdsourcing to improve both the quality and quantity of medical image datasets across different modalities. Our approach utilises a user-friendly online platform that enables a diverse group of crowd annotators to label medical images efficiently. By integrating the MedSAM segmentation AI with this platform, we accelerate the annotation process while maintaining expert-level quality through an algorithm that merges crowd-labelled images. Additionally, we employ pix2pixGAN, a generative AI model, to expand the training dataset with synthetic images that capture realistic morphological features. These methods are combined into a cohesive framework designed to produce an enhanced dataset, which can serve as a universal pre-processing pipeline to boost the training of any medical deep learning segmentation model. Our results demonstrate that this framework significantly improves model performance, especially when training data is limited.

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