A5076795164- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Cardiac Imaging and Diagnostics
- Photoacoustic and Ultrasonic Imaging
- Advanced Neural Network Applications
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- Explainable Artificial Intelligence (XAI)
- Artificial Intelligence in Healthcare and Education
- AI in cancer detection
- Neonatal Respiratory Health Research
- Dental Radiography and Imaging
- Retinopathy of Prematurity Studies
- Retinal Imaging and Analysis
- Medical Imaging and Analysis
- COVID-19 diagnosis using AI
- Single-cell and spatial transcriptomics
- Neonatal and fetal brain pathology
Indian Institute of Technology Madras
2019-2025
Radboud University Nijmegen
2021
Friedrich Miescher Institute
2021
University of Zurich
2021
Healthcare Technology Innovation Centre
2019-2021
Imperial College London
2021
Institute for Biomedical Engineering
2021
Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder, which affects the developing peripheral retinal vasculature premature infants. Early detection ROP possible in stage 1 and 2 characterized by demarcation line ridge with width separates vascularised retina retina. To detect line/ from neonatal images complex task because low contrast images. In this paper we focus on ridge, important landmark diagnosis, using Convolutional Neural Network(CNN). Our contribution to use...
<title>Abstract</title> The human brain is believed to contain a full complement of neurons by the time birth together with substantial amount connectivity architecture, even though significant growth occurs postnatally. developmental process leading this outcome not well understood in humans comparison model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but cellular resolution. In contrast, sparsely sampled histological or spatial omics...
Deep generative models and synthetic medical data have shown significant promise in addressing key challenges healthcare, such as privacy concerns, bias, the scarcity of realistic datasets. While research this area has grown rapidly demonstrated substantial theoretical potential, its practical adoption clinical settings remains limited. Despite benefits offers, questions surrounding reliability credibility persist, leading to a lack trust among clinicians. This position paper argues that...
X-ray coronary angiography (XCA) is a principal approach employed for identifying disorders. Deep learning-based networks have recently shown tremendous promise in the diagnosis of disorder from XCA scans. A deep edge adaptive instance normalization style transfer technique segmenting arteries, presented this paper. The proposed combines with dense extreme inception network and convolution block attention module to get best artery segmentation performance. We tested method on two publicly...
Abstract The human brain is believed to contain a full complement of neurons by the time birth together with substantial amount connectivity architecture, even though significant growth occurs postnatally. developmental process leading this outcome not well understood in humans comparison model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided...
X-ray coronary angiography (XCA) is a principal approach employed for identifying disorders. Deep learning-based networks have recently shown tremendous promise in the diagnosis of disorder from XCA scans. A deep edge adaptive instance normalization style transfer technique segmenting arteries, presented this paper. The proposed combines with dense extreme inception network and convolution block attention module to get best artery segmentation performance. We tested method on two publicly...