Yixiao Zhang

Orcid: 0000-0002-2451-4589

Affiliations:
  • Johns Hopkins University, Baltimore, MD, USA


According to our database1, Yixiao Zhang authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images.
IEEE Trans. Pattern Anal. Mach. Intell., September, 2024

Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography.
Medical Image Anal., 2024

2023
Continual Learning for Abdominal Multi-organ and Tumor Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation.
Proceedings of the Medical Optical Imaging and Virtual Microscopy Image Analysis, 2022

Fast AdvProp.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation.
CoRR, 2021

Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.
J. Digit. Imaging, 2020

C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

When Radiology Report Generation Meets Knowledge Graph.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020


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