29 citations · 111 across the 23 of their papers we have counts for
23 papers
X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data
Xinquan Yang, Jinheng Xie, Yawen Huang +6
Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges. Despite the recent strides in diffusion-based methods for enhancing the representatio…
Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning
Ziqi Zhang, Yuexiang Li, Yawen Huang +6
Recent studies have uncovered a new research line, namely source-free domain adaptation, which adapts a model to target domains without using the source data. Such a setting can ad…
TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
Minye Shao, Xingyu Miao, Haoran Duan +7
3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods…
Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
Minye Shao, Zeyu Wang, Haoran Duan +5
Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate…
URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction
Jie Song, Yue Sun, Ziyun Cai +3
The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditiona…
Wearable-based behaviour interpolation for semi-supervised human activity recognition
Haoran Duan, Shidong Wang, Varun Ojha +5
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level represe…
AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion
Jie Hu, Yawen Huang, Yilin Lu +4
Anomaly synthesis is one of the effective methods to augment abnormal samples for training. However, current anomaly synthesis methods predominantly rely on texture information as…
Learning Long-form Video Prior via Generative Pre-Training
Jinheng Xie, Jiajun Feng, Zhaoxu Tian +8
Concepts involved in long-form videos such as people, objects, and their interactions, can be viewed as following an implicit prior. They are notably complex and continue to pose c…
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
Peng Tu, Xun Zhou, Mingming Wang +7
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoin…
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
Xingyu Miao, Yang Bai, Haoran Duan +4
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition. This work aims to achieve zero-shot controlled stylization in 3D…
CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from Monocular Video
Xingyu Miao, Yang Bai, Haoran Duan +4
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performa…
Federated Learning via Input-Output Collaborative Distillation
Xuan Gong, Shanglin Li, Yuxiang Bao +6
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data. Existi…
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Xingyu Miao, Yang Bai, Haoran Duan +5
Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. How…
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
Guoyang Xie, Jinbao Wang, Yawen Huang +4
The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural feature…
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
A New Perspective to Boost Vision Transformer for Medical Image Classification
Yuexiang Li, Yawen Huang, Nanjun He +2
Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale lab…
FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs
Peng Tu, Xu Xie, Guo AI +3
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors. However, some vision…
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Haoqin Ji, Haozhe Liu, Yuexiang Li +7
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed…
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Yao Zhang, Nanjun He, Jiawei Yang +6
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possibl…
Robust Representation via Dynamic Feature Aggregation
Haozhe Liu, Haoqin Ji, Yuexiang Li +5
Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse…
Cross-Modality Neuroimage Synthesis: A Survey
Guoyang Xie, Yawen Huang, Jinbao Wang +4
Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modali…
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
Jinbao Wang, Guoyang Xie, Yawen Huang +3
The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of we…
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images
Yu Chen, Jiawei Chen, Dong Wei +2
Deep learning models, such as the fully convolutional network (FCN), have been widely used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple modalit…