most citedCycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2024

Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans

Bizhe Bai, Yan-Jie Zhou, Yujian Hu +5

Pulmonary embolism (PE) is a life-threatening condition where rapid and accurate diagnosis is imperative yet difficult due to predominantly atypical symptomatology. Computed tomogr…

cs.CV2024

Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

Weiwei Cao, Jianpeng Zhang, Yingda Xia +7

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hinder…

eess.IV20241 cited

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

Wei Fang, Yuxing Tang, Heng Guo +9

In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution. The lowered…

cs.CV2024

Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration

Tony C. W. Mok, Zi Li, Yunhao Bai +9

Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-gu…

cs.CV2023

Unsupervised 3D registration through optimization-guided cyclical self-training

Alexander Bigalke, Lasse Hansen, Tony C. W. Mok +1

State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised le…