Limited-supervision for efficient medical image understanding

动机：深度学习模式正在推动大多数视觉识别任务的进步，在医疗保健、自动驾驶或安全等战略领域具有巨大而有价值的潜力。虽然这些技术在完全监督的情况下取得了出色的性能，但这些模型在实际应用中存在两大障碍：对新任务的泛化性能较差，以及对顺序任务进行训练时可能会丢失学习知识。理想情况下，将训练过的模型扩展到新类别将需要收集和标记新类别的额外数据，并重新启动对整个新数据集的训练程序。然而，这种情况是不现实的。因此，需要将当前的学习算法提高到一个全新的水平，使模型能够快速适应新的任务(即，使用较少的标记样本)并避免知识遗忘。研究目标和方法：该研究计划将有助于为视觉应用开发新的学习策略，以解决深层模型的这两个关键挑战，主要关注语义分割。我们打算利用未标记的图像数据和先前的领域知识，这些在当前的方法中被低估了。这将增强深层模型的表示能力，在可用标签数据很少的情况下产生更具概括性的特征。为了减少任务之间的灾难性干扰--按顺序学习--我们将研究新的注意机制和元记忆的使用，迫使网络关注将在任务中保留的与类别无关的参数。鉴于医学图像给机器学习方法带来的独特挑战(例如，复杂的形状、高变异性等)以及它们在医疗保健方面的巨大潜力，本研究计划将重点放在医学图像上。其影响：我预计该计划将带来强大的学习方法，以在不同的挑战性场景中理解和解释医学成像数据的内容，例如低标签数据制度和顺序训练，例如在分散的系统中。从长远来看，我的目标是将特定的人类行为转移到计算机上，从图像解释的角度来看，这样他们就可以有效地从少量样本中学习，并避免在按顺序学习不同任务时出现显著的记忆损失。对这些图像的稳健解释将为许多疾病的诊断、治疗和后续治疗提供宝贵的支持。尽管设计的方法可以应用于广泛的应用，但我们将优先考虑神经学、心脏病学和肿瘤学，这些学科具有很高的经济和社会影响，我们在这方面有相当多的经验。除了本程序采用的应用领域外，这些新的学习策略将对语义分割领域产生影响，特别是在广泛的学科范围内缺乏完整注释的情况下。

Motivation: Deep learning models are driving progress in most visual recognition tasks, having an enormous and valuable potential in strategic domains such as health-care, autonomous driving or security. While these techniques have achieved outstanding performance when full-supervision is available, there exist two major obstacles to deploy these models in practical applications: poor generalization performance on new tasks and potential loss of learned knowledge when training on sequential tasks. Ideally, expanding a trained model to novel classes would require collecting and labeling additional data for new categories and restart the training procedure on the entire novel dataset. Nevertheless, this scenario is unrealistic. Thus, there is a need to bring current learning algorithms to a whole new level where models can quickly adapt to new tasks (i.e., with few labeled samples) and avoid knowledge forgetting. Research objectives and methodology: This research program will contribute to develop novel learning strategies for visual applications that address these two key challenges on deep models, with a primary focus on semantic segmentation. We intend to leverage unlabeled image data and prior domain knowledge, which have been underestimated in current approaches. This will enhance the representation power of deep models, leading to more generalizable features when few labeled data is available. To reduce the catastrophic interference between tasks -learned sequentially-, we will investigate novel attention mechanisms and the use of meta-memories, forcing the network to focus on class-agnostic parameters which will be retained across tasks. This research program will focus on medical images, given the unique challenges they bring to machine learning methods (e.g., complex shape, high variability, etc), and their tremendous potential for healthcare.   Impact: I expect that this program will lead to robust learning methods to understand and interpret the content of medical imaging data in different challenging scenarios, such as low labeled data regime and sequential training, e.g., in decentralized systems. In the long-term, I aim at transferring specific human behaviour to computers, from an image interpretation perspective, so that they can efficiently learn from few samples and avoid significant memory losses when learning different tasks sequentially. Robust interpretation of these images will provide invaluable support for diagnosis, treatment and follow-up of many diseases. Even though devised methods can be applied to a breadth of applications, we will prioritize neurology, cardiology and oncology, which have a high economical and social impact and for which we have a considerable experience. Beyond the application domain adopted in this program, these novel learning strategies will make an impact on the area of semantic segmentation, particularly when full annotations are scarce, in a broad span of disciplines.