With only four labeled volumes, adding boundary prediction as an auxiliary task raises left atrium segmentation Dice from 0.587 to 0.774, exceeding semi-supervised baselines that have access to only four unlabeled volumes.
Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment
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abstract
This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds. Then classifiers are trained to predict whether the value to be regressed is less than or equal to each of these thresholds. The different outputs of the classifiers are then concatenated in the form of an additional vector of variables that enriches the initial vector of the regression problem. The implemented system can thus be considered as a generic pre-processing tool. We tested the proposed enrichment method with 5 types of regressors and evaluated it in 33 regression datasets. Our experimental results confirm the interest of the approach.
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BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes
With only four labeled volumes, adding boundary prediction as an auxiliary task raises left atrium segmentation Dice from 0.587 to 0.774, exceeding semi-supervised baselines that have access to only four unlabeled volumes.