H. Ravishankar, N. Paluru, P. Sudhakar and P. Yalavarthy, TTA-FM: Patient-Specific Test-Time Adaptation using Foundation Models for Improved Prostrate Segmentation in Magnetic Resonance Images, ISBI 2024, Athens, Greece.
R. Sathish, R. Venkataramani, C. Aladahalli, K S Shriram and P. Sudhakar, Boundary- Aware Uncertainty for Automatic Caliper Placement, SPIE Medical Imaging 2024, San Diego, USA.
R. Sathish, R. Venkataramani, K S Shriram and P. Sudhakar, Task-driven prompt evolution for Foundation models, in MICCAI 2023 1st International Workshop on Foundation Models for General Medical AI, Vancouver, Canada.
H. Ravishankar, R. Patil, D. Anand, V. Singhal, U. Agrawal, R. Venkataramani and P. Sudhakar, Stochastic Weight Perturbations along the Hessian: A Plug and Play Method to Compute Uncertainty, in UNSURE-MICCAI 2022, Singapore.
D. Anand, P. Annangi and P. Sudhakar, Benchmarking Self-Supervised Representation Learning from a Million Cardiac Ultrasound Images, in EMBC 2022, Glasgow, Scotland.
P. Annangi, P. Sudhakar and M. Washburn, From 2D Ultrasound to Patient-Specific 3D Surface Models for Interventional Guidance , in EMBC 2022, Glasgow, Scotland.
H. Ravishankar, P. Sudhakar and P. Yalavarthy, Unsupervised Inference-Time Patient Specific Adaptation Method for Generalized Deep Semantic Segmentation, submitted to IEEE Journal of Biomedical and Health Informatics.
D. Anand, R. Patil, U. Agrawal, R. Venkataramani and P. Sudhakar Towards Generalization of Medical Imaging AI-models: Sharpness-aware minimizers and beyond, ISBI 2022.
P. Sudhakar, R. Langoju, A. Narayanan, V. Chaugule, V. Amilneni, P. Cheerankal and B. Das. Self-supervised Deep Learning for CT Deconvolution, SPIE Medical Imaging 2021.
U. Agrawal, A. Hegde, R. Langoju, P. Sudhakar, B. D. Patil, R. K. Sundar and B. Das, Enhancing z-resolution in Axial CT Volumes with Deep Residual Learning, SPIE Medical Imaging 2021.
H Ravishankar, R Venkataramani, S Anamandra, P Sudhakar and P Annangi, Feature Transformers: Privacy Preserving Lifelong Learners for Medical Imaging, in MICCAI 2019, Shenzen, China.
H. Ravishankar, R. Venkataramani, S. Thiruvenkadam, P. Sudhakar and V. Vaidya, Learning and Incorporating Shape Models for Semantic Segmentation, MICCAI 2017, Quebec city, Canada.
R. Venkataramani, S. Thiruvenkadam, P.Sudhakar, H. Ravishankar and V. Vaidya, Filter sharing: Efficient learning of parameters for volumetric convolutions, NIPS 2016 Workshop on Machine Learning for Healthcare, Barcelona, Spain.
H. Ravishankar, P. Sudhakar, R. Venkataramani, S. Thiruvenkadam, P. Annangi, B. Narayanan, V. Vaidya, Understanding the Mechanisms of Deep Transfer Learning for Medical Images, MICCAI 2017 Workshop on Deep Learning for Medical Image Analysis, Athens, Greece.
Sparsity and Compressed Sensing
P. Sudhakar, L. Jacques, X. Dubois, P. Antoine and L. Joannes, Compressive imaging and characterization of sparse light deflection maps, SIAM Journal on Imaging Sciences, 8(3), 1824-1856, 2015.
P. Sudhakar, L. Jacques, A. Gonzalez, X. Dubois, P. Antoine and L. Joannes, Compressive
acquisition of sparse deflectometric maps, in SampTA 2013, Bremen, Germany.
A. Benichoux, P. Sudhakar, F. Bimbot and R. Gribonval, Well-posedness of the frequency permutation problem in sparse filter estimation with lp minimization, Applied and Computational Harmonic Analysis, 5(3), pp. 359-540, November 2013.
P. Sudhakar, L. Jacques, X. Dubois, P. Antoine and L. Joannes, Compressive schlieren deflectometry, in ICASSP
2013, Vancouver, Canada.
A. Benichoux, P. Sudhakar and R. Gribonval, Well-posedness of the frequency permutation
problem in sparse filter estimation with lp minimization, in SPARS11, Edinburgh, Scotland, June
27-30, 2011.
Signal and Image Processing
A. Adiga, S. Mulleti, P. Sudhakar and C. S. Seelamantula, Two-Dimensional FRI Signal Reconstruction Using Blind Deconvolution, SampTA 2015.
P. Sudhakar and P. K. Ghosh, Recognition benefit of articulatory features from acoustic-to-articulatory inversion using sparse smoothing, INTERSPEECH 2014, Singapore.
P. Sudhakar, L. Jacques and P. K. Ghosh, A sparse smoothing approach for Gaussian mixture
model based acoustic-to-articulatory inversion, ICASSP 2014, Florence, Italy.
S. Prasad and K. R. Ramakrishnan, On resampling detection and its application to detect image
tampering, in IEEE International Conference on Multimedia and Expo (ICME 2006), July 2006.
Blind Source Separation
A. Benichoux, P. Sudhakar, F. Bimbot and R. Gribonval, Some uniqueness results in sparse
convolutive source separation, in International Conference on Latent Variable Analysis and Source
Separation, Mar 2012, Tel Aviv, Israel.
S. Arberet, P. Sudhakar and R. Gribonval, Estimating multiple filters from stereo mixtures: a
double sparsity approach, in SPARS11, Edinburgh, Scotland, June 27-30, 2011.
S. Arberet, P. Sudhakar and R. Gribonval, Wideband Doubly-Sparse Approach for MITO Sparse
Filter Estimation, in ICASSP 2011, May 2011.
P. Sudhakar, S. Arberet and R. Gribonval, Double Sparsity: Towards Blind Estimation of Multiple Channels, in Latent Variable Analysis and Signal Separation, 9th International Conference on
(LVA/ICA2010), September 2010.
P. Sudhakar and R. Gribonval, Sparse filter models for solving permutation indeterminacy in convolutive blind source separation, in SPARS09 - Signal Processing with Adaptive Sparse Structured
Representations, April 2009.
P. Sudhakar and R. Gribonval, A sparsity-based method to solve the permutation indeterminacy
in frequency domain convolutive blind source separation, in ICA 2009, 8th International Conference
on Independent Component Analysis and Signal Separation, March 2009.
Neuroscience
P. Sudhakar, R. Madhavan, R. Mullick, E. T. Tan and S. Joel, Method to functionally parcellate
the brain consistently across subjects, to appear in OHBM 2016, Geneva.
P. Sudhakar, R. Madhavan, R. Mullick, E. T. Tan and S. Joel, Reproducibility of group spectral
clustering of the sensorimotor cortex, to appear in OHBM 2016, Geneva.
Theses
Prasad Sudhakar, Sparse Models and Convex Optimisation for Convolutive Blind Source Separation, 2011, Ph.D. thesis, University of Rennes 1, France.
Prasad S., Signal processing algorithms for digital image forensics, 2007, M.Sc. thesis, Indian Institute of Science, Bangalore.