Major Bio-Medical Imaging Modalities

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! :-)

Head CT - ICH

Lung/Chest CT - TransNodNet

Abdominal CT - ABDSeg, Sarcopenia, GBSeg

DeReCon

DEXA

MOSeg

Dermotology

SkinCancer

SfS3DD

AAR

CliPhAI

Psoriasis

Colonoscopy - Colonopolyps, Stereo, Registration, PolypSeg, Quality, Celiac, Tumor, Ulcer, ArcEndos, CryptFoci

Capsule endoscopy - MucosaSeg, 3D-SfS, Stamping, Illumination, CE-Polyps, Bleeding, Distortion, Compression, Summary

Chromoendoscopy - FICE, NBI

StromaSeg

Glioma

NucleiSeg

SpaTrans

Ulcer

EsoCount

Liver Fibrosis

LungMAP

RDCRN

Breast Cancer

Epifluorescence - Denoising, Segmentation, Clustering

Immunofluorescence - RF-HEp-2, HEp-2SegZoo

Fundoscopy - Retinal-seg

Confocal - Denoising, Deconvolution, Segmentation

ConIFSeg

IHC

FISH

Cryo-EM - Denoising

ErythroNet

MALDI-MS

ErythroNet

Brain MRI - MAC-Multiphase segmentation, MSP-Mid saggital plane, Skull stripping, Symmetry, SIMMER, Bleeds, Denoising, iSPi

Liver MRI

MREntNet

MRA

fMRI - LeaS

Mammography - Segmentation, Enhancement, Registration

DBT

Thermal imaging

Denoising

Segmentation

Liver Stiffness

PylStenNet

AbsCellUSNet

PEchoNet

Cerebral Palsy - Gait Analysis

GMA

DLVD

3D-Cell-Tracking

Denoising


Chest X-ray (CXR) - PICCLineNet, TracNet, Analysis, LungSeg, LungBoundary, TBScreen

Cardiomegaly

Organ Segmentation

CBXIR

DEXA

Multimodal AI

Multimodal data that spans the domains of Bioinformatics and Clinical, Health, Medical Informatics can be leveraged in unified frameworks and Prasath Lab is interested in leveraging the recent multimodal AI (contrastive learning, foundational models) to solve biomedical informatics problems.


Selected publications: