Our projects involve robust Machine Learning (ML)/Deep Learning (DL)/Artificial Intelligence (AI) techniques and we apply them to various bio-medical imaging modalities in an organ/disease-agnostic manner. We do not discriminate against any type of data/organs/diseases as we strongly believe in "Building Healthcare - Data as Capital" motto! :-)

Biomedical Image/Video Processing and Analysis

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),  High Angular Resolution Diffusion Imaging (HARDI), 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), 3D imaging. 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.