Major Research Areas/Interests/Projects - Bioinformatics
Application of signal processing, image processing, computer vision and machine learning techniques to biomedical data - Magnetic Resonance (MR), Computed Tomography (CT), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Optical Coherence Tomography (OCT), X-ray, Dual energy X-ray absorptiometry (DEXA), Ultrasound (US), Portable Ultrasound (POCUS), Wireless Capsule Endoscopy (CE), Colonoscopy, Histopathology, Confocal, Fluorescence, Magnetic Resonance Enterography (MRE), Magnetic Resonance Angiography (MRA), Fluorescein Angiogram (FA), Color Fundus, Two-photon Microscopy, Mass Spectrometry Imaging (MSI), Matrix-assisted Laser Desorption/Ionization (MALDI), Mammography, Cryo-EM, cDNA Microarray images, Laryngeal High-Speed Videos, Electroencephalography (EEG), Electrocardiography (ECG), MR Elastography, Calcium Imaging, Fluorescence lifetime imaging microscopy (FLIM), loop-mediated isothermal amplification (LAMP). Image reconstruction, compressed sensing, enhancement, noise removal, compression of endoscopic videos, image segmentation based on active contours, shape based (Shape from Shading, Structure from Motion) approaches for endoscopic images. Machine learning, and deep learning for biomedical image analysis - Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), Recurrent Neural Network (RNN), Encoder-Decoder, Transformers, Diffusion Models, Contrastive Learning, Foundation Models. Bioinformatics, visualization and interpretation of biomedical imaging data. Segmentation and quantitative image analysis for magnetic resonance, histopathological, fluorescence microscopy images. Imaging biomarkers, E-health, telemedicine, m-health related data processing. Computational neuroscience, transcranial direct current stimulation (tDCS), fMRI analysis. Clinical/federated learning implementation of machine learning, deep learning models for imaging and multimodal data, clinical informatics. Application of signal processing, image processing, computer vision and machine learning techniques to clinical and patient's video analysis. AI for CP/TBI patient's rehabilitation, gait pathology detection. Video-based action recognition, fall detection, motion analysis, optical flow.
Endoscopy : MucosaSeg, 3D-SfS, Stamping, Illumination, CE-Polyps, Bleeding, Distortion, Compression, Summary, Colonopolyps, Stereo, Registration, Polypseg, Quality, Celiac, Tumor, Ulcer, CryptFoci, ArcEndos, Chromoendoscopy - FICE, NBI
II. AI/ML/DL for Bioinformatics/Computational Biology/MultiModal Bioinformatics
Application of machine/deep learning to single cell RNA sequence (scRNAseq), Assay for Transposase-Accessible Chromatin using sequence (ATAC-seq), etc. Analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq). Deep learning, explainable AI for computational biology problems - quality control (QC), clustering, denoising, imputation, trajectory inference, immunogenicity prediction, RNA binding protein prediction, transcription factor binding prediction, Gene regulatory network (GRN) inference, antimicrobial resistance prediction, CyTOF, flow and mass cytometry. Analysis of multiplexed imaging, mass spectrometry imaging (MSI), CODEX imaging, spatial transcriptomics in tandem with single-cell genomics data and in general multi-omics, and multi-modal analysis. Exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data. Automated cell population discovery, exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data.
Application of natural language processing, signal processing, image processing, computer vision and machine learning techniques to clinical, heath, and medical informatics problems. AI for NICU/PICU/ICU/CICU data, Critical Care, Emergency Medicine, Sepsis, Bronchopulmonary Dysplasia. Healthcare NLP for EHR/EMR/ePHI/PHR/PRO Data - Unstructured text data analysis with ML/DL - Clinical data - MLDevOps + FHIR. AI for Healthcare and Clinical Implementation. AI for NICU/PICU/ICU/CICU.
EHR : RareDiseases, ASD
Others: CPIID, mHealth
Note that these contain prior major research projects in the areas of Applied Mathematics, Image Processing, and Computer Vision 2004 to 2018 i.e. before the start of Prasath Lab @ CCHMC in March 2018. Although we continue to work on these areas the major focus of our lab are now in various Bio + Medical Informatics (BMI) areas.
I. Analysis & PDEs with Image Processing Applications
Nonlinear, anisotropic diffusion partial differential equations (PDEs), Weak/viscosity/dissipative/Young measure solutions, Perona-Malik type diffusion PDEs, variable exponent PDEs, p-Laplacian, p(t,x)-Laplacian, complex diffusion, higher order PDEs, adaptive PDEs and computational methods (finite differences, finite elements) for solving them are major themes. Linear, nonlinear scale space theory and applications - smoothing, denoising, deblurring, super-resolution, fusion, segmentation, decomposition.
III. Remote Sensing, Biometrics, Other Image Processing/Computer Vision Problems and Data Science Domains
Image speckle denoising, segmentation for SAR, PolSAR images. Road network extraction from aerial imagery. Wavelets, Shearlets for image processing. Regression analysis, Robust M-estimators, Discontinuity adaptive smoothing schemes and kernel smoothing. Image and data fusion, multi-focus fusion, multi-sensor fusion, sensor networks. Biometrics - ocular, periocular, fingerprint, iris, retina, face, palm print. Multi-view geometry, shape from X, segmentation, optical flow, mosaicing, blending, registration, point cloud processing, tracking, large scale 3D reconstruction for full motion video (FMV), wide area motion imagery (WAMI), video surveillance, summarization, event detection. DTM/DEM, edge detection, super-resolution, deblocking, decompression, saliency detection, watermarking, steganography, Kinect depth data processing, local binary patterns, registration, video data analysis. Feature analysis, deep learning for image processing and computer vision problems. Sensor networks with emphasize on visual sensors, internet of things (IoT), natural language processing (NLP), text mining, summarization, affective computing, sentiment analysis from text, social media data, emotion recognition from image data. Topological data analysis (TDA) for signals, imaging and biomedical informatics problems.
Remote Sensing: Shadows, STLLT, PolSARSeg, Clouds, Roads, WAMI
Image quality : MSID, BriCho