Search Results for author:
Found 15 papers, 7 papers with code
Architecture Disentanglement for Deep Neural Networks
Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs.
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation.
Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems.
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Image Super-Resolution Using Dense Skip Connections
In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network.
Enhancement Mask for Hippocampus Detection and Segmentation
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.
Stain Style Transfer using Transitive Adversarial Networks
It is not necessary for an expert to pick a representative reference slide in the proposed TAN method.
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
To alleviate this problem, we propose a quantitative statistical framework for FCCM, comprising an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module.
