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Found 11 papers, 2 papers with code
Generate to Understand for Representation
In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks.
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features.
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
The development of medical image segmentation using deep learning can significantly support doctors' diagnoses.
Compound Domain Generalization via Meta-Knowledge Encoding
Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.
Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids
Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions.
Chemical Physics
Adaptive noise imitation for image denoising
Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients.
Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity
This work is the first to train models for predicting a post{'}s stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.
MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma
Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC.
