| 2013 | MICCAI | Heterogeneity Wavelet Kinetics from DCE-MRI for Classifying Gene Expression Based Breast Cancer Recurrence Risk. | Majid Mahrooghy, Ahmed Bilal Ashraf, Dania Daye, Carolyn Mies, Michael D. Feldman, Mark Rosen, Despina Kontos |
| 2011 | MICCAI | A Multichannel Markov Random Field Approach for Automated Segmentation of Breast Cancer Tumor in DCE-MRI Data Using Kinetic Observation Model. | Ahmed Bilal Ashraf, Sara Gavenonis, Dania Daye, Carolyn Mies, Michael D. Feldman, Mark Rosen, Despina Kontos |
| 2010 | MICCAI | Imaging as a Surrogate for the Early Prediction and Assessment of Treatment Response through the Analysis of 4-D Texture Ensembles (ISEPARATE). | Peter Maday, Parmeshwar Khurd, Lance Ladic, Mitchell D. Schnall, Mark Rosen, Christos Davatzikos, Ali Kamen |
| 2010 | MICCAI | Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy. | Pallavi Tiwari, John Kurhanewicz, Mark Rosen, Anant Madabhushi |
| 2009 | MICCAI | Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data. | Pallavi Tiwari, Mark Rosen, Galen Reed, John Kurhanewicz, Anant Madabhushi |
| 2008 | MICCAI | Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy. | Pallavi Tiwari, Mark Rosen, Anant Madabhushi |
| 2008 | MICCAI | Multi-Attribute Non-initializing Texture Reconstruction Based Active Shape Model (MANTRA). | Robert Toth, Jonathan Chappelow, Mark Rosen, Sona Pungavkar, Arjun Kalyanpur, Anant Madabhushi |
| 2007 | MICCAI | A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS). | Pallavi Tiwari, Anant Madabhushi, Mark Rosen |
| 2006 | ECCV | Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI. | Anant Madabhushi, Jianbo Shi, Michael D. Feldman, Mark Rosen, John Tomaszewski |
| 2005 | MICCAI | Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer. | Anant Madabhushi, Jianbo Shi, Mark Rosen, John Tomaszewski, Michael D. Feldman |