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Learning With Unsure Data for Medical Image Diagnosis

In image-based disease prediction, it can be hard to give certain cases a deterministic "disease/normal" label due to lack of enough information, e.g., at its early stage. We call such cases "unsure" data. Labeling such data as unsure suggests follow-up examinations so as to avoid irreversible medical accident/loss in contrast to incautious prediction. This is a common practice in clinical diagnosis, however, mostly neglected by existing methods. Learning with unsure data also interweaves with two other practical issues: (i) data imbalance issue that may incur model-bias towards the majority class, and (ii) conservative/aggressive strategy consideration, i.e., the negative (normal) samples and positive (disease) samples should NOT be treated equally \-- the former should be detected with a high precision (conservativeness) and the latter should be detected with a high recall (aggression) to avoid missing opportunity for treatment. Mixed with these issues, learning with unsure data becomes particularly challenging. In this paper, we raise "learning with unsure data" problem and formulate it as an ordinal regression and propose a unified end-to-end learning framework, which also considers the aforementioned two issues: (i) incorporate cost-sensitive parameters to alleviate the data imbalance problem, and (ii) execute the conservative and aggressive strategies by introducing two parameters in the training procedure. The benefits of learning with unsure data and validity of our models are demonstrated on the prediction of Alzheimer's Disease and lung nodules.

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