Major highlights of the Imran Lab in 2025

A Year in Review: 2025

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In 2025, The Imran Lab demonstrated remarkable progress across research, collaborations, and various other activities. The group’s output, significantly bolstered by external collaborations, included numerous high-impact publications and presentations. Key achievements for the year include 18 published/accepted journal articles and conference papers. Lab members delivered several presentations at various forums throughout the year. Dr. Imran received notable recognition by being selected as a Lighthouse Beacon Foundation Scholar.

Publications

  • [J1] T. Ward, M. Owen, O. Coleman, B. Noehren, A. Imran, “Autoadaptive medical segment anything,” Manuscript accepted in Nature Scientific Reports, 2025. (preprint, code)
  • [J2] M. Medrano, S. Wang, L. Sun, A. Imran, J. Cao, G. Stevens, J. Tse, A. Wang, “Scout-Dose-TCM: Direct and prospective scout-based estimation of personalized organ doses from tube current modulated CT exams,” Physics in Medicine and Biology (PMB), 2025. (paper)
  • [J3] T. Ward, A. Moseley, A. Imran, “Domain and task-focused example selection for data-efficient contrastive medical image segmentation,” Manuscript accepted in the Journal of Machine Learning for Biomedical Imaging (MELBA), 2025. (preprint, code)
  • [J4] M. Gokmen, B. Arslan, C. Bumgardner, A. Imran, “An effective image despeckling and reconstruction approach using U-Net based model and comparative analysis,” Nature Scientific Reports, 2025. (paper)
  • [J5] O. Oladimeji, A. Imran, X. Wang, S. Unnikrishnan, “Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective,” Image and Vision Computing, 2025. (paper)
  • [J6] K. Rifa, M. Ahamed, J. Zhang, A. Imran, “TFKT V2: Task-focused knowledge transfer from natural images for computed tomography perceptual image quality assessment,” _Journal of Medical Imaging (JMI), 2025. (paper, code)
  • [J7] W. Lee, F. Wagner, A. Galdran, Y. Shi, W. Xia, G. Wang, X. Mou, M. Ahamed, A. Imran, J. Oh, K. Kim, J. Baek, D. Lee, B. Hong, P. Tempelman, D. Lyu, A. Kuiper, L. van Blokland, M. Calisto, S. Hsieh, M. Han, J. Baek, A. Maier, A. Wang, G. Gold, J. Choi, “Low-dose computed tomography perceptual image quality assessment,” Medical Image Analysis (MedIA), 2025. (paper, code)
  • [J8] S. Wang, M. Medrano, A. Imran, W. Lee, J. Cao, G. Stevens, J. Tse, A. Wang, “Automated estimation of individualized organ-specific dose and noise from clinical CT scans,” Physics in Medicine and Biology (PMB), 2025. (paper)
  • [C1] K. Rifa, J. Zhang, A. Imran, “U-KAN-Seg: Improving universal text-driven CT image segmentation with Kolmogorov-Arnold networks,” Paper accepted at SPIE Medical Imaging: Image Processing, 2026.
  • [C2] T. Ward, A. Imran, “A probabilistic segment anything model for ambiguity-aware medical image segmentation,” Paper accepted at SPIE Medical Imaging: Imaging Informatics, 2026. (preprint, code)
  • [C3] A. Mahanipour, A. Imran, H. Khamfroush, “Towards memory-efficient foundation models in medical imaging: A federated learning and knowledge distillation approach,” Paper accepted at The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance, NeurIPS,, 2025. (paper)
  • [C4] A. Mahanipour, A. Imran, H. Khamfroush, “Federated reprogramming knowledge distillation for medical image classification,” Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning. MICCAI, 2025. (paper)
  • [C5] T. Ward, A. Imran, “Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold networks,” Medical Imaging with Deep Learning (MIDL), Salt Lake City, UT, USA, 2025. (paper, code)
  • [C6] N. Munia, A. Imran, “Class-N-Diff: Classification-induced diffusion can make fair skin cancer diagnosis,” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025. (paper, code)
  • [C7] K. Rifa, J. Zhang, A. Imran, “Swin-KAT: Advancing Swin Transformer with Kolmogorov-Arnold network for CT image quality assessment,” IEEE International Symposium on Biomedical Imaging (ISBI), 2025. (paper, code)
  • [C8] N. Munia, A. Imran, “Prompting medical vision-language models to mitigate diagnosis bias by generating realistic dermoscopic images,” IEEE International Symposium on Biomedical Imaging (ISBI), 2025. (preprint, code)
  • [C9] T. Ward, A. Imran, “Annotation-efficient task guidance for medical segment anything,” IEEE International Symposium on Biomedical Imaging (ISBI), 2025. (preprint, code)
  • [C10] K. Rifa, M. Ahamed, J. Zhang, A. Imran, “Task-focused knowledge transfer from natural images for CT image quality assessment,” SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2025. (paper, code)
  • [S1] J. Zhang, G. Ge, A. Imran, “Advancing CT image quality assessment with deep learning: From traditional metrics to clinically informed AI evaluation,” Radiological Society of North America (RSNA): Scientific Assembly and Annual Meeting, Chicago, IL, USA, 2025.
  • [S2] N. Munia, J. Zhu, O. Nasraoui, A. Imran, “Differential attention for multimodal crisis event analysis,” CVPR 2025 Workshop on Multimodal Foundation Models (MMFM), Nashville, TN, USA, 2025. (paper)
  • [S3] T. Ward, B. McFarland, S. Nozad, T. Arshad, H. Nebbache, J. Chen, X. Wang, A. Imran, “Automated intraoperative lumpectomy margin detection using SAM-incorporated forward-forward contrastive learning,” Medical Imaging with Deep Learning (MIDL), Salt Lake City, UT, USA, 2025. (paper)
  • [S4] J. Zhang, K. Rifa, C. Weaker, G. Ge, A. Imran, “Deep learning-based metric vs global noise for CT image quality assessment,” American Association of Physicists in Medicine (AAPM), 2025, Washington, D.C.
  • [S5] M. Massey, A. Imran, “Decoding Earth’s surface: The role of multimodal large language models,” Workshop on Computer Vision for Geospatial Image Analysis at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
  • [T1] M. Massey, “Exploring multimodal AI techniques to support surficial geologic mapping,” MS Thesis, University of Kentucky, Lexington, KY, USA
  • [D1] M. Massey, A. Imran, “EarthScape AI Dataset,” Kentucky Geological Survey, ser. 14, research data, 2025.
  • [P1] N. Munia, A. Imran, “Class-N-Diff: Classification-induced diffusion model can make fair skin cancer diagnosis,” arXiv preprint arXiv:2510.16887, October 19, 2025, 1–6.
  • [P2] T. Ward, A. Imran, “A probabilistic segment anything model for ambiguity-aware medical image segmentation,” arXiv preprint arXiv:2509.05809, September 2025, 1–6.
  • [P3] N. Munia, J. Zhu, O. Nasraoui, A. Imran, “Differential attention for multimodal crisis event analysis,” arXiv preprint arXiv:2507.05165, July 2025, 1-9.
  • [P4] T. Ward, M. Owen, O. Coleman, B. Noehren, A. Imran, “Autoadaptive medical segment anything,” arXiv preprint arXiv:2507.01828, July 2025, 1-11.
  • [P5] M. Medrano, S. Wang, L. Sun, A. Imran, J. Cao, G. Stevens, J. Tse, A. Wang, “Scout-Dose-TCM: Direct and prospective scout-based estimation of personalized organ doses from tube current modulated CT exams,” arXiv preprint arXiv:2506.24062, June 2025, 1-20.
  • [P6] T. Ward, X. Wang, B. McFarland, M. Ahamed, S. Nozad, T. Arshad, H. Nebbache, J. Chen, A. Imran, “Detection of breast cancer lumpectomy margin with SAM-incorporated forward-forward contrastive learning,” arXiv preprint arXiv:2506.21006, June 2025, 1-19.
  • [P7] T. Ward, A. Moseley, A. Imran, “ Domain and task-focused example selection for data-efficient contrastive medical image segmentation,” arXiv preprint arXiv:2505.19208, May 2025, 1-14.
  • [P8] T. Ward, A. Imran, “Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold networks,” arXiv preprint arXiv:2504.03923, April 2025, 1–18.
  • [P9] N. Munia, A. Imran, “Prompting medical vision-language models to mitigate diagnosis bias by generating realistic dermoscopic images,” arXiv preprint arXiv:2504.01838, April 2025, 1–6.
  • [P10] N. Munia, A. Imran, “DermDiff: Generative diffusion model for mitigating racial biases in dermatology diagnosis,” arXiv preprint arXiv:2503.17536, March 2025, 1–11.
  • [P11] M. Massey, A. Imran, “EarthScape: A multimodal dataset for surficial geologic mapping and earth surface analysis,” arXiv preprint arXiv:2503.15625, March 2025, 1–17.
  • [P12] T. Ward, A. Imran, “Annotation-efficient task guidance for medical segment anything,” arXiv preprint arXiv:2412.08575, December 2024, 1–6.

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Presentations

  • Dr. Imran presented on task-focused knowledge transfer for CT IQA at SPIE Medical Imaging 2025 in San Diego, CA
  • Tyler presented the functional brain analysis and FFCL-SAM work at the 2025 Medical Imaging with Deep Learning (MIDL 2025), Salt Lake City, UT
  • Tyler, Munia, and Ramisa presented at the 2025 IEEE International Symposium on Biomedical Imaging (ISBI 2025) in Houston, TX
  • Tyler, Munia, and Ramisa presented at the 2025 CCTS Spring Conference in Lexington, KY
  • Tyler and Munia presented on probabilistic SAM and Generative Dermatology Diffusion at the 2025 MIDL Young Researcher Showcase event
  • Munia presented on differential attention for multimodal crisis event analysis at CVPR 2025 as part of the MMFM workshop in Nashville, TN
  • Munia presented at the 2025 CLIMBS SuperCollider event in Lexington, KY
  • Munia gave a talk on the DermDiff and follow-up work at the departmental Keeping Current Seminar (KCS)
  • Dr. Imran presented on Image Quality through Informatics at the 2025 Society of Pediatric Radiology (SPR)’s Informatics Course
  • Dr. Imran presented on End-to-End CT imaging at the Center for Clinical and Translational Science
  • Dr. Imran presented the Class-N-Diff work at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025) in Copenhagen, Denmark

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Awards, Recognition, Leadership, Etc.

  • Dr. Imran was selected as a Lighthouse Beacon Foundation Scholar
  • Dr. Imran was awarded a Pilot Grant from the Center for Clinical and Translational Science
  • Dr. Imran received a Radiology Pilot Grant as a Co-PI with Dr. Zhang
  • Dr. Imran served as an Area Chair for MICCAI 2025 and MIDL 2025
  • Dr. Imran served on the Best Poster Committee at MIDL 2025
  • Dr. Imran participated in the NIH Imaging Technology Development (ITD) Study Section Review
  • Best Oral Presentation award at the Annual UK Radiology Clinical Research Showcase;
    Deep learning–based CT image quality assessment for abdominal protocol optimization in animal and clinical studies: toward clinical integration co-authored by Siddique, A. (presenter), Rifa, K., Ganesh, H., Lee, J., Ge, G., Imran, A., and Zhang, J.
  • Tyler received the Verizon Communications Graduate Fellowship

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Milestones

  • Matt graduated with an MS in Data Science, producing a thesis on multimodal AI for surficial geologic mapping
  • Munia passed her PhD qualifying exam
  • Tyler passed his PhD qualifying exam
  • Milin graduated and joined Carnegie Mellon University’s MS in Computational Finance program
  • Dr. Imran developed and taught the new AI in the World (CS 263) course in Spring and Fall of 2025
  • EarthScape AI Dataset (co-authored by Massey, M. and Imran, A.) was released via UKNOWLEDGE