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Anant Madabhushi
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| Address: |
599 Taylor Road (Bldg# 3893) |
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Piscataway, NJ 08854 |
| Room: |
BME-213 |
| Phone: |
732-445-4500 x6213 |
| Email: |
anantm AT RCI dot RUTGERS dot EDU |
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| Disease informatics from the macro- to micro-scale is increasingly available for a range of detection, diagnostic, and therapy recommendation (theragnostic) applications tailored to each patient's history and current condition.
With the advent of multi-modal, multi-parametric high resolution radiological imaging providing anatomical, biochemical, and physiological information, it has become increasingly important to identify the potential value of this information in the pre-operative or pre-therapeutic cancer population. At the Laboratory for Computational Imaging and Informatics at Rutgers University we have been focussed on developing image, spectral analysis and machine learning methods for efficient analysis and correlation of disease signatures across multiple scales and modalities -- from gene expression to histopathology to radiology with an emphasis on prostate cancer and more recently breast cancer. Our systems approach for quantitatively analysing multi-functional, multi-scale, multi-modal data in an integrated fashion will be extremely important in diagnostic, theragnostic (predicting response to therapy), and prognostic settings. |
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Recent Papers:
1. Lee, G., Rodriguez, C, Madabhushi, A, "Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene- and Protein-Expression Studies" IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2008 http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.36
2. Souza, A., Udupa, J, Madabhushi, A, Use of Generalized Scale for image filtering, Med. Image Anal. 2007, doi:10.1016/j.media.2007.07.007.
3. Madabhushi, A, Udupa, J, Souza, A, Generalized Scale: Theory, Algorithms and Application to Inhomogeneity Correction, Computer Vision and Image Understanding, vol. 101, pp. 100-121, Feb. 2006
Website
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