The primary focus of my lab is developing novel medical image analysis and machine learning tools for imaging data, through spatial correlation and cross-linking against pathology or molecular data.
Applications of our tools are being examined in:
- Decision support for treatment (e.g. choice of therapy)
- Targeting therapeutic procedures (e.g. guiding ablation, radiotherapy, surgery)
- Biological quantitation for treatment response characterization in vivo
This multi-disciplinary, multi-pronged approach is being applied to colorectal, renal, and prostate cancers, as well as digestive diseases.
Viswanath, S+, Tiwari P+, Lee, G+, Madabhushi, A, Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases. BMC Med Imaging. 2017 Jan 5;17(1):2. (+joint first authors) [PMID: 28056889, PMCID: PMC5217665]
Antunes, J, Viswanath, S, Rusu, M, Valls, L, Hoimes, C, Avril, N, Madabhushi, A, Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study.
Antunes J., Prasanna P., Madabhushi A., Tiwari P., Viswanath S. (2017) RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10434. Springer, Cham