The primary focus of my lab is developing new image analytics, radiomics, and machine learning schemes for imaging data. The key innovation lies in handcrafting and designing unique tools that can capture biologically relevant and clinically intuitive measurements from routinely acquired imaging (MRI, CT, PET). Further, by conducting cross-scale associations between imaging, pathology, and -omics through multimodal fusion, these tools can “unlock” the information captured by different data modalities.
Applications of our tools are being examined in:
- Computer-aided diagnosis & disease characterization
- Decision support for treatment (e.g. choice of therapy) and targeting therapeutic procedures
- Quantitative evaluation of response to treatment in vivo
This multi-disciplinary, multi-pronged approach is being applied to colorectal, renal, and prostate cancers, as well as digestive diseases.
Awards and Honors
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