Heng Guo

Orcid: 0000-0002-4069-4743

Affiliations:
  • DAMO Academy, Alibaba Group, Hangzhou, China


According to our database1, Heng Guo authored at least 11 papers between 2022 and 2025.

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

Timeline

Legend:

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Bibliography

2025
M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision.
CoRR, September, 2025

A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT.
CoRR, March, 2025

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies With Query Embedding.
IEEE J. Biomed. Health Informatics, January, 2025

Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-supervision, Mixture of Experts and Multi-task Integration.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model Using 3D Whole-Body CT Scans.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Representing Topological Self-similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures.
Proceedings of the Computer Vision - ECCV 2024, 2024

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Segmentation.
CoRR, 2023

Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation.
CoRR, 2022

Semi-supervised Detection, Identification and Segmentation for Abdominal Organs.
Proceedings of the Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, 2022


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