386 citations · 846 across the 22 of their papers we have counts for
10 papers · 1 filter
DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization
Feng Hou, Jin Yuan, Ying Yang +7
Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-langu…
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
Yang Liu, Yao Zhang, Yixin Wang +5
Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points…
A Robust Ensemble Model for Patasitic Egg Detection and Classification
Yuqi Wang, Zhiqiang He, Shenghui Huang +1
Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method. The development of deep…
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Yao Zhang, Jiawei Yang, Yang Liu +6
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. How…
A Survey of Visual Transformers
Yang Liu, Yao Zhang, Yixin Wang +7
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pi…
Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes
Yunjie Peng, Kang Ma, Yang Zhang +1
Gait recognition captures gait patterns from the walking sequence of an individual for identification. Most existing gait recognition methods learn features from silhouettes or ske…
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
Jiawei Yang, Yao Zhang, Yuan Liang +3
Deep learning models are notoriously data-hungry. Thus, there is an urging need for data-efficient techniques in medical image analysis, where well-annotated data are costly and ti…
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Yixin Wang, Zihao Lin, Zhe Xu +7
Medical imaging plays a pivotal role in diagnosis and treatment in clinical practice. Inspired by the significant progress in automatic image captioning, various deep learning (DL)…
Double-Uncertainty Weighted Method for Semi-supervised Learning
Yixin Wang, Yao Zhang, Jiang Tian +4
Though deep learning has achieved advanced performance recently, it remains a challenging task in the field of medical imaging, as obtaining reliable labeled training data is time-…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…