论文标题
Multimix：从医学图像中进行的很少监督，极端的多任务学习
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
论文作者
论文摘要
通过从有限数量的标记数据中学习的半监督学习已被研究为被监督对应物的替代方案。从大量未标记的数据收益中最大化知识收益，使半监督的学习设置。此外，在同一模型中学习多个任务进一步提高了模型的通用性。我们提出了一种新型的多任务学习模型，即多组，该模型以少量监督的方式共同学习疾病分类和解剖学分割，同时通过两项任务之间的桥梁显着性来保持解释性。我们在训练集中使用不同数量的标记数据进行的广泛实验证明了我们的多任务模型对肺炎分类和从胸部X射线图像分割肺部的有效性。此外，整个任务中的内域和跨域评估都进一步展示了我们模型的潜力，以适应具有挑战性的概括场景。
Semi-supervised learning via learning from limited quantities of labeled data has been investigated as an alternative to supervised counterparts. Maximizing knowledge gains from copious unlabeled data benefit semi-supervised learning settings. Moreover, learning multiple tasks within the same model further improves model generalizability. We propose a novel multitask learning model, namely MultiMix, which jointly learns disease classification and anatomical segmentation in a sparingly supervised manner, while preserving explainability through bridge saliency between the two tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images. Moreover, both in-domain and cross-domain evaluations across the tasks further showcase the potential of our model to adapt to challenging generalization scenarios.
