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- Chang, Qi, et al.
(författare)
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DeepRecon : Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method
- 2022
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Ingår i: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. - Cham : Springer Nature Switzerland. - 0302-9743 .- 1611-3349. - 9783031164392 ; 13434 LNCS, s. 567-577
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Konferensbidrag (refereegranskat)abstract
- Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental in building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures using the optimal latent representation. We further explore downstream applications of 3D shape reconstruction and 4D motion pattern adaptation by the different latent-space manipulation strategies. The simultaneously generated high-resolution images present a high interpretable value to assess the cardiac shape and motion. Experimental results demonstrate the effectiveness of our approach on multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D motion pattern adaption performance.
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| 2. |
- Dou, Shihan, et al.
(författare)
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Decorrelate Irrelevant, Purify Relevant : Overcome Textual Spurious Correlations from a Feature Perspective
- 2022
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Konferensbidrag (refereegranskat)abstract
- Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones. Most of the existing debiasing methods often identify and weaken these samples with biased features (i.e., superficial surface features that cause such spurious correlations). However, down-weighting these samples obstructs the model in learning from the non-biased parts of these samples. To tackle this challenge, in this paper, we propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective. Specifically, we introduce Random Fourier Features and weighted re-sampling to decorrelate the dependencies between features to mitigate spurious correlations. After obtaining decorrelated features, we further design a mutual-information-based method to purify them, which forces the model to learn features that are more relevant to tasks. Extensive experiments on two well-studied NLU tasks demonstrate that our method is superior to other comparative approaches.
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