News/2025
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2025
February
- Congratulations to Carlos Rivas on winning a Best Paper Award at the Proceedings of SPIE Medical Imaging (SPIE-MI 2025), San Diego, CA, February 16–20, 2025.
- IACL papers at the Proceedings of SPIE Medical Imaging (SPIE-MI 2025), San Diego, CA, February 16–20, 2025.
- Carlos Rivas presented "Unique MS lesion identification from MRI".
- Shimeng Wang presented "Longitudinal analysis of cerebellum volume changes in a multiple sclerosis cohort".
- Dr. Jinwei Zhang presented "“Bi-directional MS lesion filling and synthesis using denoising diffusion implicit model-based lesion repainting".
- Xiaojian Chen's paper titled "Correlation ratio for unsupervised learning of multi-modal deformable registration", was presented by Dr. Jinwei Zhang.
- Other accepted papers at SPIE-MI 2025 coming from collaborations with IACL.
- Dr. Lianrui Zuo from Vanderbilt University, presented "Counterfactual brain amyloid pathology image synthesis".
| IACL at SPIE-MI 2024 | ||||
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| Dr. Yihao Liu presenting Yuli Wang's work titled "Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound". | Zhangxing Bian presenting "Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?". | |||
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| Savannah P. Hays and Dr. Yihao Liu saying "Ayyy". | Samuel W. Remedios and Savannah P. Hays showing some SPIE pride. | |||
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| IACL Lab members (new and old) at SPIE-MI 2024. | Samuel W. Remedios presenting "Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation". | |||
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| Junyi Liu presenting her poster titled "Exploratory magnetic resonance elastography synthesis from magnetic resonance and diffusion tensor imaging". | ||||
| IACL at the Johns Hopkins School of Medicine and Whiting School of Engineering Research Retreat 2024 | ||||||
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January
- Yihao Liu successfully defends his thesis titled "Methods for Automated Analysis of OCT and OCTA Images".
- Lianrui Zuo successfully defends his thesis titled "Unsupervised structural MRI harmonization by learning disentangled representations".
