
Chang Liu
Erlangen National High-Performance Computing Center (NHR@FAU)
Research associates
Address
Contact
- Email: chang.ch.liu@fau.de
Chang was a doctoral researcher at the Pattern Recognition Lab at FAU until March 2025. His research focused on medical image processing and analysis, particularly the automated segmentation of computed tomography (CT) images and the generation of high-quality CT images. Beyond his core research, he has also contributed to applying AI technologies in various fields, including second language education and nail disease diagnosis. Before his PhD, he studied Medical Engineering at FAU and gained practical experience also in the industry.
Since April 1st, Chang will be part of NHR@FAU, where he joins the AI group to support AI-oriented projects across diverse research fields. His work aims to enhance the success of research projects with our modern HPC systems.
- Since 04/2025:
Research Associate at NHR@FAU - Since 03/2020:
Researcher at Pattern Recognition Lab, FAU - Since 09/2016:
Student at FAU
Student | Title | Type | Status |
---|---|---|---|
Yaqiong Ni | Deep Learning-Based Breast Cancer Risk Stratification Using Multiple Instance Learning on LDCT Scans | MA thesis | running |
Yan Wang | Improving manual annotation of 3D medical segmentation dataset using SAM2 | MA thesis | finished |
Junliang Kang | Automated Configuration of U-Net Architecture for Medical Image Segmentation | MA thesis | finished |
Yugashree Chaudhari | Automatic Data Augmentation for Multi organ Segmentation | MA thesis | finished |
Tianyi Wang | A disentangled representation strategy to enhance multi-organ segmentation in CT using multiple datasets | MA thesis | finished |
Dinuo wei | Latent Diffusion Model for CT Synthesis | Project | running |
Christopher Brückner | Evaluation of imperfect segmentation labels and the influence on deep learning models | BA thesis | finished |
Nazmus Sakib | Ground Truth based Convolution Kernel Initialization Method for Medical Image Segmentation | MA thesis | finished |
2025
Advancement and independent validation of a deep learning-based tool for automated scoring of nail psoriasis severity using the modified nail psoriasis severity index
In: Frontiers in Medicine 12 (2025), Article No.: 1574413
ISSN: 2296-858X
DOI: 10.3389/fmed.2025.1574413
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Anatomy-aware Data Augmentation for Multi-organ Segmentation in CT: AnatoMix
German Conference on Medical Image Computing, 2025 (Regensburg, 2025-03-09 – 2025-03-11)
In: Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (ed.): Bildverarbeitung für die Medizin 2025. Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025, Wiesbaden: 2025
DOI: 10.1007/978-3-658-47422-5_29
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2024
Cut to the Mix: Simple Data Augmentation Outperforms Elaborate Ones in Limited Organ Segmentation Datasets
27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 (Marrakesh, MAR, 2024-10-06 – 2024-10-10)
In: Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2024
DOI: 10.1007/978-3-031-72111-3_14
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Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization
In: Scientific Reports 14 (2024), p. 1-11
ISSN: 2045-2322
DOI: 10.1038/s41598-024-59731-y
URL: https://www.nature.com/articles/s41598-024-59731-y
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Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis
In: RMD Open 10 (2024)
ISSN: 2056-5933
DOI: 10.1136/rmdopen-2024-004273
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Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning
German Conference on Medical Image Computing, BVM 2024 (Erlangen, 2024-03-10 – 2024-03-12)
In: Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (ed.): Bildverarbeitung für die Medizin 2024. BVM 2024, Wiesbaden: 2024
DOI: 10.1007/978-3-658-44037-4_76
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2023
Clinical prototype implementation enabling an improved day-to-day mammography compression
In: Physica Medica 106 (2023), Article No.: 102524
ISSN: 1120-1797
DOI: 10.1016/j.ejmp.2023.102524
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Whole-Body Multi-Organ Segmentation Using Anatomical Attention
20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Cartagena, COL, 2023-04-18 – 2023-04-21)
In: Proceedings – International Symposium on Biomedical Imaging 2023
DOI: 10.1109/ISBI53787.2023.10230529
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Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
In: The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023) 2023
DOI: 10.48550/arXiv.2307.07168
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2022
Organ-Specific vs. Patient Risk-Specific Tube Current Modulation in Thorax CT Scans Covering the Female Breast
7th International Conference on Image Formation in X-Ray Computed Tomography (Virtual, Online, 2022-06-12 – 2022-06-16)
In: Joseph Webster Stayman (ed.): Proceedings of SPIE – The International Society for Optical Engineering 2022
DOI: 10.1117/12.2646582
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Patient-specific radiation risk-based tube current modulation for diagnostic CT
In: Medical Physics (2022)
ISSN: 0094-2405
DOI: 10.1002/mp.15673
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Multi-organ Segmentation with Partially Annotated Datasets
In: Bildverarbeitung für die Medizin 2022, Wiesbaden: Springer Vieweg, 2022, p. 216-221
ISBN: 9783658369316
DOI: 10.1007/978-3-658-36932-3_46
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2021
Differential diagnosis of RA and PsA using neural networks on 3D bone shape of finger joints
EULAR 2021 Congress (Paris, 2021-06-02 – 2021-06-05)
In: Annals of the Rheumatic Diseases 2021 2021
DOI: 10.1136/annrheumdis-2021-eular.383
URL: https://ard.bmj.com/content/80/Suppl_1/86
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2020
Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (, 2020-10-31 – 2020-11-07)
In: Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization 2020
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