8 citations · 11 across the 7 of their papers we have counts for
8 papers
Polarised X-ray emission from the accreting millisecond pulsar SAX J1808.4-3658 during an X-ray outburst and a type-I X-ray burst
A. Papitto, A. Di Marco, M. C. Baglio +22
Modelling the X-ray pulse profiles of accreting millisecond pulsars (AMSPs) is a key technique to constrain the masses and radii of neutron stars. To this end, measuring the proper…
Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
Yuchen Li, Ziru Wei, Zhen Zhao +2
Medical referring image segmentation (MRIS) predicts lesion masks from medical images and natural-language referring expressions, but acquiring paired pixel-level annotations and r…
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
Erjian Guo, Zhen Zhao, Zicheng Wang +3
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels an…
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
Erjian Guo, Zicheng Wang, Zhen Zhao +1
Accurate medical image segmentation is often hindered by noisy labels in training data, due to the challenges of annotating medical images. Prior research works addressing noisy la…
UniBrain: A Unified Model for Cross-Subject Brain Decoding
Zicheng Wang, Zhen Zhao, Luping Zhou +1
Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific mode…
PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
Zicheng Wang, Zhenghao Chen, Yiming Wu +3
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, the…
Clean Label Disentangling for Medical Image Segmentation with Noisy Labels
Zicheng Wang, Zhen Zhao, Erjian Guo +1
Current methods focusing on medical image segmentation suffer from incorrect annotations, which is known as the noisy label issue. Most medical image segmentation with noisy labels…
Progressive Classifier and Feature Extractor Adaptation for Unsupervised Domain Adaptation on Point Clouds
Zicheng Wang, Zhen Zhao, Yiming Wu +2
Unsupervised domain adaptation (UDA) is a critical challenge in the field of point cloud analysis. Previous works tackle the problem either by feature extractor adaptation to enabl…