InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation

We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities. InfiNet consists of double encoder arms for T1 and T2 input scans that feed into a joint-decoder arm that terminates in the classification layer. The novelty of InfiNet lies in the manner in which the decoder upsamples lower resolution input feature map(s) from multiple encoder arms. Specifically, the pooled indices computed in the max-pooling layers of each of the encoder blocks are related to the corresponding decoder block to perform non-linear learning-free upsampling. The sparse maps are concatenated with intermediate encoder representations (skip connections) and convolved with trainable filters to produce dense feature maps. InfiNet is trained end-to-end to optimize for the Generalized Dice Loss, which is well-suited for high class imbalance. InfiNet achieves the whole-volume segmentation in under 50 seconds and we demonstrate competitive performance against multiple state-of-the art deep architectures and their multi-modal variants.Comment: 4 pages, 3 figures, conference, IEEE ISBI, 201

Two-stage decisions increase preference for hedonic options

When choosing from multiple options, decision-makers may directly choose an option (single-stage decision), or initially shortlist a subset of options, and then choose an option from this shortlist (two-stage decision). Past work suggests that these two decision formats should lead to the same final choice when information about the choice alternatives is held constant. In contrast, this research demonstrates a novel effect: two-stage decisions increase preference for hedonic (vs. utilitarian) options. A regulatory focus account explains this effect. In a two-stage process, after shortlisting, decision-makers feel that they have sufficiently advanced their prevention goals, and this reduces their prevention focus during the final choice stage. Reduced prevention focus, in turn, enhances hedonic preference. Four studies across different decision contexts illustrate this effect and support the underlying process mechanism. The findings suggest that the formal structure of a decision (single-stage vs. two-stage) leads to systematic differences in decision-makers’ choices