Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China.
School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia.
Comput Biol Med. 2023 Feb;153:106496. doi: 10.1016/j.compbiomed.2022.106496. Epub 2022 Dec 28.
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.
深度学习的复兴为各种任务提供了有前景的解决方案。虽然传统的深度学习模型是为单一特定任务构建的，但同时能够完成至少两个任务的多任务深度学习（MTDL）已引起研究关注。MTDL 是一种联合学习范式，利用多个相关任务之间的固有相关性，在提高性能、增强通用性和降低整体计算成本方面实现互惠互利。
本篇综述重点介绍 MTDL 在医学图像计算和分析中的高级应用。我们首先总结了四种流行的 MTDL 网络架构（即级联、并行、交互和混合）。然后，我们回顾了基于 MTDL 的网络在八个应用领域的代表性应用，包括大脑、眼睛、胸部、心脏、腹部、骨骼肌肉、病理学和其他人体部位。虽然基于 MTDL 的医学图像处理蓬勃发展，并在许多任务中表现出色，但在某些任务中仍存在性能差距，因此我们感知到了开放的挑战和未来的趋势。例如，在 2018 年的缺血性脑卒中病变分割挑战赛中，级联 MTDL 模型报告的最高骰子分数为 0.51，最高召回率为 0.55，这表明需要进一步研究努力来提高当前模型的性能。
