About MIVIC is a research group in the Division of Physics, Engineering, Mathematics and Computer Science in Delaware State University. MIVIC's research interests include medical image analysis, mathematical methods for visual computing, machine learning, and visualization. More information about the group's research and training activities, publications, and group members can be found below: Research
NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architectures Submitted by admin on Tue, 05/05/2026 - 13:06 Y. Wang, Makrogiannis, S., and Kambhamettu, C., “NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architectures”. 2026.
Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions Submitted by admin on Tue, 05/05/2026 - 13:05 L. Carlos Alm Da Silva, Kaymaz, B., Makrogiannis, S., Rogers, K. J., Kecskemethy, H. H., Nikam, R., Bowen, J. Richard, Gould, S. W., and Thacker, M. M., “Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions”, Journal of Pediatric Orthopaedics B, vol. 35, pp. 164–172, 2026.
Breast density classification and decision explainability by deep sparse approximations Submitted by admin on Tue, 05/05/2026 - 13:03 C. E. Harris, Liu, L., Kennady, D. Adhithya, Bakic, P. R., and Makrogiannis, S., “Breast density classification and decision explainability by deep sparse approximations”, in Medical Imaging 2026: Computer-Aided Diagnosis, 2026.
Segmentation of thigh muscle groups in volumetric fat- and water-suppressed MRI by a hierarchical transformer model Submitted by admin on Tue, 05/05/2026 - 12:59 M. N. Ibrahim, Liu, L., Annasamudram, N. V., Fishbein, K., Spencer, R. G., and Makrogiannis, S., “Segmentation of thigh muscle groups in volumetric fat- and water-suppressed MRI by a hierarchical transformer model”, in Medical Imaging 2026: Image Processing, 2026.
Foundation models for healthcare: innovations in generative AI, computer vision, language models, and multimodal systems Submitted by admin on Thu, 01/15/2026 - 17:52 S. Makrogiannis, “Foundation models for healthcare: innovations in generative AI, computer vision, language models, and multimodal systems”, Frontiers in Computer Science, vol. 7, p. 1744581, 2025.
Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions Submitted by admin on Thu, 01/15/2026 - 17:51 L. Carlos Alm Da Silva, Kaymaz, B., Makrogiannis, S., Rogers, K. J., Kecskemethy, H. H., Nikam, R., Bowen, J. Richard, Gould, S. W., and Thacker, M. M., “Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions”, Journal of Pediatric Orthopaedics B, pp. 10–1097, 2025.
Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy Submitted by admin on Thu, 07/24/2025 - 12:15 N. Annasamudram, Zhao, J., Oluwadare, O., Prashanth, A., and Makrogiannis, S., “Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy”, Scientific Reports, vol. 15, p. 11717, 2025.
Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays Submitted by admin on Thu, 07/24/2025 - 12:13 C. E. Harris, Liu, L., Almeida, L., Kassick, C., and Makrogiannis, S., “Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays”, Bone Reports, vol. 25, p. 101845, 2025.
Pediatric osteopenia prediction and interpretation in wrist x-rays Submitted by admin on Thu, 07/24/2025 - 12:12 C. E. Harris, Liu, L., and Makrogiannis, S., “Pediatric osteopenia prediction and interpretation in wrist x-rays”, in Medical Imaging 2025: Computer-Aided Diagnosis, 2025.
