Led By Prof. Anubha Gupta
SBILab focuses on Signal Processing areas including applications of Wavelet Transforms, Machine (Deep) Learning, and Compressed Sensing, Sparse Reconstruction, fMRI/EEG/MRI/DTI Signal and Image Processing, Genomics Signal Processing, Signal Processing for Communication Engineering, and RF Energy Harvesting.
Collaborators are welcomed from Academia/Industry. Kindly contact Prof. Anubha Gupta(firstname.lastname@example.org) for further details.
Available Ph.D. Positions
1) One PhD position is available under rolling admission in SBILab, Deptt. of ECE, IIIT Delhi in the area of medical cancer imaging. The work requires building deep learning based architectures for image segmentation, stain normalization, and cell classification. SBILab recently ran a medical imaging challenge at IEEE ISBI 2019 conference in Italy. Meritorious students interested to pursue full time PhD position are invited to submit their resume to email@example.com latest by July 15, 2019. The essential qualification are- M.Tech/M.E. in ECE/CSE/or related fields. Meritorious B.Tech students can also apply for the same. It is essential for the candidate to have cleared at least one national level examination of GATE/CSIR-UGC Net or similar. Details are available here.
2) A Ph.D. position is available for a motivated student, in the area of "Deep Learning in Education Technologies". The work will be in collaboration with Queensland University of Technology, Australia. Interested students especially with Ph.D. fellowships by CSIR, UGC JRF or DST-Inspire are strongly encouraged to apply. Please apply by submitting your detailed CV to firstname.lastname@example.org.
SBILab in collaboration with AIIMS, New-Delhi has released three medical imaging datasets:
1) C-NMC 2019 Dataset (also used for ISBI-2019 challenge organized by SBILab)
2) MiMM_SBILab Dataset: Microscspic Images of Multiple Myeloma Cancer
3) SN-AM Dataset: White Blood cancer dataset of B-ALL and MM for stain normalization
The datasets are publicly available at The Cancer Imaging Archive (TCIA). A detailed description of the datasets are available under Resources.