Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7. 12% on tumor Dice.
Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly.
Ranked #1 on Dialog Relation Extraction on DialogRE
Building a socially intelligent agent involves many challenges, one of which is to track the agent's mental state transition and teach the agent to make rational decisions guided by its utility like a human.
Second, we can largely boost the robustness of existing ConvNets, proved by: (i) testing on scans with synthetic pathologies, and (ii) training and evaluation on scans of different scanning setups across datasets.
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines.
Conversational Intelligence requires that a person engage on informational, personal and relational levels.
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics.
In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch.
The encoder-decoder network is widely used to learn deep feature representations from pixel-wise annotations in biomedical image analysis.