bibtext: @InProceedings{ Xu_FMABS_MICCAI2024,
                   author = { Xu, Zhe and Chen, Cheng and Lu, Donghuan and Sun, Jinghan and Wei, Dong and Zheng, Yefeng and Li, Quanzheng and Tong, Raymond Kai-yu },
                   title = { { FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Semi-supervised learning (SSL) has significantly advanced 3D medical image segmentation by effectively reducing the need for laborious dense labeling from radiologists. Traditionally focused on \textit{model-centric} advancements, we anticipate that the SSL landscape will shift due to the emergence of open-source generalist foundation models, e.g., Segment Anything Model (SAM). These generalists have shown remarkable zero-shot segmentation capabilities with manual prompts, allowing a promising \textit{data-centric} perspective for future SSL, particularly in pseudo and expert labeling strategies for enhancing the data pool. To this end, we propose the Foundation Model-driven Active Barely Supervised (FM-ABS) learning paradigm for developing customized 3D specialist segmentation models with shoestring annotation budgets, i.e., merely labeling three slices per scan. Specifically, building upon the basic mean-teacher framework, FM-ABS accounts for the intrinsic characteristics of 3D imaging and modernizes the SSL paradigm with two key data-centric designs: (i) specialist-generalist collaboration where the in-training specialist model delivers class-specific prompts to interact with the frozen class-agnostic generalist model across multiple views to acquire noisy-yet-effective pseudo labels, and (ii) expert-model collaboration that advocates active cross-labeling with notably low annotation efforts to progressively provide the specialist model with informative and efficient supervision in a human-in-the-loop manner, which benefits the automatic object-specific prompt generation in turn. Extensive experiments on two benchmark datasets show the promising results of our approach over recent SSL methods under extremely limited (barely) labeling budgets.
title: FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation
authors: Xu, Zhe Chen, Cheng Lu, Donghuan Sun, Jinghan Wei, Dong Zheng, Yefeng Li, Quanzheng Tong, Raymond Kai-yu