Weak supervision with imperfect annotations for medical image computing

医学图像计算(MIC)的目标是发展计算方法来解决与医学图像解释和理解有关的问题。机器学习的最新进展使MIC实现了巨大飞跃，我们已经开始见证它们对我们日常生活的影响，从3D人类胚胎/器官可视化到新冠肺炎肺部扫描的智能检查。此外，医疗保健行业越来越多地接受由机器学习技术支持的云服务，如Google Cloud Healthcare和IBM Watson Health。在未来，设想的MIC解决方案有望将临床医生从繁琐的日常操作中解放出来，为医生提供第二种意见，并促进生物医学研究人员发现数字生物标记物。与此同时，仍有许多障碍需要克服。在大多数现实世界的MIC任务中，由于医疗数据标签的高昂成本，获得可用于强监管方法的高质量数据集是极其困难的；相比之下，弱注释或不完美的注释更容易访问。各种MIC任务中的不完善标注使得机器学习技术在弱监督下的工作变得非常必要。我的长期目标是利用机器学习技术和计算机视觉算法来改进与医疗相关的医学成像，本研究方案旨在创新不同的弱监督算法，以提取在不完美的注释中无法明确获得的医疗信息。三个不同的主题将是本建议的主要方向，包括：(1)通过融合批量对比学习和产生式学习的表示学习方法，从稀疏、极不平衡的标注医学图像中学习异常；(2)通过将贝叶斯推理融入深度学习中，从噪声标注医学图像中学习；(3)通过开发结合类激活图方法和多实例学习的统一的弱监督感兴趣区域分割流水线，从不精确的标注医学图像中进行推理。这一建议的潜在影响包括(1)发明具有不完善注释的医学图像计算解决方案，这将为海量现有医学图像直接用于MIC研究打开大门；(2)提出有助于发现异常、罕见病例的数字生物标记物的表示学习方法。虽然我们的目标应用领域是医学图像计算，但本研究提出的解决方案也可以移植到许多图像分析和计算机视觉任务中。此外，通过这一研究计划培训的HQP将获得科学理论和面向应用的开发方面的培训，这将对他们未来的医学成像和机器学习职业生涯具有价值。

Medical image computing (MIC) aims to develop computational methods for solving problems pertinent to medical image interpretation and understanding. Recent advances in machine learning have made a big leap in MIC and we have started to witness their influence on our daily lives, ranging from 3D human embryo/organ visualization to intelligent examination of lung scans for COVID-19 checkup. Moreover, the healthcare industry is increasingly embracing the cloud services powered by machine learning techniques, such as Google Cloud healthcare and IBM Watson Health. In the future, the envisioned MIC solutions are expected to free clinicians from tedious daily operations on easy cases, to provide physicians a second opinion, and to facilitate biomedical researchers for numerical biomarker discovery. Meanwhile, there are still many hurdles to overcome. In most real-world MIC tasks, it is extremely difficult to attain high quality datasets that can be used in strong supervision methods due to the high cost of medical data labeling; in contrast, weak annotation, or imperfect annotation, is much easier to access. The imperfect annotation in various MIC tasks makes machine learning techniques working with weak supervision to be highly desired. With my long-term goal to leverage machine learning techniques and computer vision algorithms to improve medical imaging relevant healthcare, this research proposal aims to innovate different weak supervision algorithms to extract medical information that is not explicitly available in imperfect annotations. Three different topics will be the major directions in this proposal, including: (1) learning anomaly from sparse, extremely unbalanced annotated medical images, through innovating representation learning methods that fuse batch contrastive learning and generative learning; (2) learning from noisy labeled medical images, by incorporating Bayesian inference into deep learning towards a unified framework; (3) inference from imprecision annotated medical images, by developing a unified weak-supervision region-of-interest segmentation pipeline that combines class activation map methods and multiple instance learning. Potential impact of this proposal includes (1) inventing solutions for medical image computing with imperfect annotation, which will open a door for the massive existing medical images to become directly usable in MIC research and (2) proposing representation learning methods that will be helpful to discover numerical biomarkers of abnormal, rare cases. Though our target application domain is medical image computing, the solutions proposed in this research are also transferable to many image analysis and computer vision tasks. Besides, the HQP trained through this research program will get training on both scientific theories and application-oriented developments, that would be valuable in their future medical imaging and machine learning careers.