Weak supervision with imperfect annotations for medical image computing

医学图像计算（MIC）旨在开发用于解决与医学图像解释和理解相关的问题的计算方法。机器学习的最新进展使MIC取得了巨大的飞跃，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COVID-19检查的肺部扫描智能检查。此外，医疗保健行业越来越多地采用由机器学习技术提供支持的云服务，例如Google Cloud healthcare和IBM沃森健康。在未来，设想的MIC解决方案预计将使临床医生从繁琐的日常操作中解脱出来，为医生提供第二意见，并促进生物医学研究人员发现数字生物标志物。与此同时，仍有许多障碍需要克服。在大多数真实世界的MIC任务中，由于医疗数据标记的高成本，很难获得可用于强监督方法的高质量数据集;相比之下，弱注释或不完善的注释更容易访问。各种MIC任务中的不完美注释使得机器学习技术在弱监督下工作非常理想。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善医学成像相关的医疗保健，这项研究计划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。该提案的主要方向包括三个不同的主题：（1）通过融合批对比学习和生成学习的创新表示学习方法，从稀疏、极不平衡的注释医学图像中学习异常;（2）通过将贝叶斯推理融入深度学习，从有噪声的标记医学图像中学习，以实现统一的框架;（3）通过将贝叶斯推理融入深度学习，从有噪声的标记医学图像中学习异常。（3）通过开发结合类激活图方法和多实例学习的统一弱监督感兴趣区域分割流水线，从不精确注释的医学图像中进行推断。 该提案的潜在影响包括：（1）发明不完善注释的医学图像计算解决方案，这将为大量现有医学图像直接用于MIC研究打开一扇门;（2）提出表示学习方法，有助于发现异常罕见病例的数字生物标志物。虽然我们的目标应用领域是医学图像计算，但本研究中提出的解决方案也可用于许多图像分析和计算机视觉任务。此外，通过该研究计划培训的HQP将获得科学理论和面向应用的开发方面的培训，这对他们未来的医学成像和机器学习职业生涯非常有价值。

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.