bibtext: @InProceedings{ Zhe_Curriculum_MICCAI2024,
                   author = { Zheng, Xiuqi and Zhang, Yuhang and Zhang, Haoran and Liang, Hongrui and Bao, Xueqi and Jiang, Zhuqing and Lao, Qicheng },
                   title = { { Curriculum Prompting Foundation Models for Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: Adapting large pre-trained foundation models, e.g., SAM, for medical image segmentation remains a significant challenge. A crucial step involves the formulation of a series of specialized prompts that incorporate specific clinical instructions. Past works have been heavily reliant on a singular type of prompt for each instance, necessitating manual input of an ideally correct prompt, which is less efficient. To tackle this issue, we propose to utilize prompts of different granularity, which are sourced from original images to provide a broader scope of clinical insights. However, combining prompts of varying types can pose a challenge due to potential conflicts. In response, we have designed a coarse-to-fine mechanism, referred to as curriculum prompting, that progressively integrates prompts of different types. Through extensive experiments on three public medical datasets across various modalities, we demonstrate the effectiveness of our proposed approach, which not only automates the prompt generation process but also yields superior performance compared to other SAM-based medical image segmentation methods. Code will be available at: https://github.com/AnnaZzz-zxq/Curriculum-Prompting.
title: Curriculum Prompting Foundation Models for Medical Image Segmentation
authors: Zheng, Xiuqi Zhang, Yuhang Zhang, Haoran Liang, Hongrui Bao, Xueqi Jiang, Zhuqing Lao, Qicheng