A5041549836- Radiomics and Machine Learning in Medical Imaging
- Privacy-Preserving Technologies in Data
- Artificial Intelligence in Healthcare and Education
- Brain Tumor Detection and Classification
- Medical Imaging and Analysis
- Acoustic Wave Resonator Technologies
- Advanced Neural Network Applications
- Glioma Diagnosis and Treatment
- Ethics in Clinical Research
- Colorectal Cancer Screening and Detection
- COVID-19 diagnosis using AI
- AI in cancer detection
- Advanced biosensing and bioanalysis techniques
- Medical Image Segmentation Techniques
- MRI in cancer diagnosis
- Privacy, Security, and Data Protection
- Machine Learning in Healthcare
- Cancer Genomics and Diagnostics
- Photonic and Optical Devices
- Sensor Technology and Measurement Systems
Indian Institute of Technology Mandi
2023-2024
Intel (United States)
2022-2023
Abstract Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing the evidence-based practice of medicine, personalizing patient treatment, reducing costs, improving both provider experience. Unlocking this requires systematic, quantitative evaluation performance medical AI models on large-scale, heterogeneous data capturing diverse populations. Here, meet need, we introduce MedPerf, an open platform for benchmarking in domain....
Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine (ML) and deep (DL) projects without sharing sensitive data, such as patient records, financial or classified secrets.Approach.Open federated (OpenFL) framework an open-source python-based tool for training ML/DL algorithms using the data-private collaborative of FL, irrespective use case. OpenFL works with pipelines built both TensorFlow PyTorch, can be easily extended other ML...
Deep Learning (DL) has the potential to optimize machine learning in both scientific and clinical communities. However, greater expertise is required develop DL algorithms, variability of implementations hinders their reproducibility, translation, deployment. Here we present community-driven Generally Nuanced Framework (GaNDLF), with goal lowering these barriers. GaNDLF makes mechanism development, training, inference more stable, reproducible, interpretable, scalable, without requiring an...
Abstract Objective. De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates need for sharing primary patient across collaborating institutions. This highlights consistent harmonized curation, pre-processing, and identification of regions interest based on uniform criteria. Approach. Towards this end, manuscript describes Fe derated T umor S egmentation (FeTS) tool, terms software architecture functionality. Main results. The aim FeTS...
Using medical imaging as case-study, we demonstrate how Intel-optimized TensorFlow on an x86-based server equipped with 2nd Generation Intel Xeon Scalable Processors large system memory allows for the training of memory-intensive AI/deep-learning models in a scale-up configuration. We believe our work represents first deep neural network having footprint (~ 1 TB) single-node server. recommend this configuration to scientists and researchers who wish develop large, state-of-the-art AI but are...
The report demonstrates the benefits (in terms of improved claims loss modeling) harnessing value Federated Learning (FL) to learn a single model across multiple insurance industry datasets without requiring themselves be shared from one company another. application FL addresses two most pressing concerns: limited data volume and variety, which are caused by privacy concerns, rarity claim events, lack informative rating factors, etc.. During each round FL, collaborators compute improvements...
Abstract BACKGROUND Diffuse astrocytic glioma are common and aggressive malignant primary brain tumors with grim prognosis. Artificial intelligence (AI) has shown promise across predictive, prognostic, diagnostic neuro-oncology applications, towards improving patient management. However, clinical translation deployment hampered by AI models’ requirements for explicit acceleration cards, which not typically considered in environments. Here, we seek the execution of models such...
Federated learning is a paradigm that enables organizations to collaborate on machine projects without sharing sensitive patient data. This lowers the barrier international collaboration build generalizable models mitigate bias by allowing access larger and more diverse datasets. The talk will highlight key considerations in federated discuss results of largest federation healthcare institutions developed state-of-the-art brain tumor boundary detection model using MRI scans from 71 across...
This paper introduces a novel spoof surface plasmon polariton (SSPP) based metamaterial-inspired electromagnetic (EM) biosensor for non-invasive diabetes analysis. The sensor design incorporates whispering gallery mode (SS-WGM) resonator, which effectively localizes the electric field within specific region due to its slow-wave propagation. To achieve optimal sensitivity, PDMS microfluidic channel is utilized hold sample (SUT) precisely on of maximum EM concentration. Two different samples,...