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Found 8 papers, 1 papers with code
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations
Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.
Structure-aware registration network for liver DCE-CT images
In addition, such paired organ segmentations are not always available in DCE-CT images due to the flow of contrast agents.
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Thus, the two trajectories can act as a bridge to link the fixed and the moving images, and guide their registration.
Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity
In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance.
