
Dr Yuanhong Chen
Postdoc Researcher
Australian Institute for Machine Learning
Division of Research and Innovation
Dr. Yuanhong Chen is a postdoctoral researcher at the Australian Institute for Machine Learning, specialising in computer vision, multimodal learning, and generative models. He holds a PhD in Computer Science and works on bridging medical image analysis and audio-visual perception with deep learning. His current research explores integrating large language models for cross-modal understanding and reasoning.
My previous research focused on multimodal learning, audio-visual perception, and spatial audio generation. My current research explores the integration of large language models for cross-modal understanding and reasoning, with applications in audio-visual learning and generative modelling.
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Education
Date Institution name Country Title 2021 - 2024 University of Adelaide Australia PhD -
Research Interests
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Journals
Year Citation 2025 Wang, C., Liu, F., Chen, Y., Frazer, H., & Carneiro, G. (2025). Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation. IEEE Transactions on Medical Imaging, 44(6), 2568-2580.
Scopus32024 Nishimura, A., Senoue, H., Mae, H., Hanyu, R., & Hu, E. (2024). CO<inf>2</inf> Reduction Performance with Double-Layered Cu/TiO<inf>2</inf> and P<inf>4</inf>O<inf>10</inf>/TiO<inf>2</inf> as Photocatalysts under Different Light Illumination Conditions. Catalysts, 14(4), 16 pages.
Scopus12024 Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Peña-Solorzano, C., . . . Carneiro, G. (2024). BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations. Medical Image Analysis, 96, 103192-1-103192-13.
Scopus5 Europe PMC12024 Lu, Y., Lin, B., Chai, S., Wang, H., Zhou, J., Hu, J., . . . Wu, L. (2024). A pyroptosis-enhanced leucocyte-hitchhiking liposomal nanoplatform for potentiated immunotherapy of hepatocellular carcinoma. Materials Today Nano, 27, 100492.
Scopus12024 Wang, C., Chen, Y., Liu, F., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2024). An Interpretable and Accurate Deep-learning Diagnosis Framework Modelled with Fully and Semi-supervised Reciprocal Learning. IEEE Transactions on Medical Imaging, 43(1), 392-404.
Scopus19 Europe PMC32023 Frazer, H. M. L., Tang, J. S. N., Elliott, M. S., Kunicki, K. M., Hill, B., Karthik, R., . . . McCarthy, D. J. (2023). ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets. Radiology: Artificial Intelligence, 5(2), 1-7.
Scopus20 WoS3 Europe PMC142023 Chen, Y., Liu, Y., Wang, H., Liu, F., Wang, C., Frazer, H., & Carneiro, G. (2023). Unraveling Instance Associations: A Closer Look for Audio-Visual
Segmentation.2023 Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2023). Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis, 90, 102930-1-102930-11.
Scopus24 Europe PMC32017 Griffits, S., Hines, S., Moloney, C., & Ralph, N. (2017). Characteristics and processes of clinical reasoning in nurses and factors related to its use: a scoping review protocol. Jbi Database of Systematic Reviews and Implementation Reports, 15(12), 2832-2836.
Scopus12 Europe PMC6 -
Conference Papers
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Preprint
Year Citation 2025 Chen, Y., Shimada, K., Simon, C., Ikemiya, Y., Shibuya, T., & Mitsufuji, Y. (2025). CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural
Audio Generation.2024 Chen, Y., Wang, C., Liu, Y., Wang, H., & Carneiro, G. (2024). CPM: Class-conditional Prompting Machine for Audio-visual Segmentation. 2023 Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2023). BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete
Annotations.2022 Chen, Y., Liu, F., Wang, H., Wang, C., Tian, Y., Liu, Y., & Carneiro, G. (2022). BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray
Classification.2022 Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2022). Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder..
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