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Found 11 papers, 2 papers with code
Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generation
We then fuse the dynamic disease topic labels with the original visual features of the images to highlight the abnormal regions in the original visual features to alleviate the visual data bias problem.
Cross-domain Robust Deepfake Bias Expansion Network for Face Forgery Detection
In addition, to further heighten the amplification of forged clues, BENet incorporates a Latent-Space Attention (LSA) module.
Identifying Distribution Network Faults Using Adaptive Transition Probability
A novel approach is suggested for improving the accuracy of fault detection in distribution networks.
Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing
Based on homogeneous tanh-transforms, we propose an occlusion-aware convolutional neural network for occluded face parsing.
Shape-Margin Knowledge Augmented Network for Thyroid Nodule Segmentation and Diagnosis
It embeds shape and margin characteristics through numerical computation and models the relationship between the thyroid nodule diagnosis results and segmentation masks.
Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
In this paper, we propose an attack type robust face anti-spoofing framework under light flash, called ATR-FAS.
Stone Needle: A General Multimodal Large-scale Model Framework towards Healthcare
Our method is a general multimodal large-scale model framework, integrating diverse modalities and allowing us to tailor for specific tasks.
A Novel Structured Natural Gradient Descent for Deep Learning
More specifically, we reconstruct the structure of the deep neural network, and optimize the new network using traditional gradient descent (GD).
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
In the first stage, the feature-extraction module is embedded into a classifier network that is trained on a large data set of GGO and non-GGO patches, which are generated by performing data augmentation from a small number of annotated CT scans.
Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality
The experiments on various classification applications, including handwritten digit recognition, lung nodule classification, face verification and face recognition, demonstrate that the proposed approach is promising to effectively deal with the problem of learning on the data with different quality and leads to the significant and stable improvements in the classification accuracy.
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
The learning in the first stage is performed on the whole input to obtain an initial deep network for tumor segmenta-tion.
