AI in Radiology and Radiomics 101: Resources and Roadmap
I have a lot of colleagues who want to learn AI to improve their radiology practice. They frequently ask me how to learn radiomics, machine learning, and deep
Artificial Intelligence
NVIDIA MAISI: Generate Synthetic CT Images with AI
NVIDIA MAISIhttps://docs.nvidia.com/nim/medical/maisi/latest/overview.html can generate synthetic 3D CT images up to 512×512×768 voxels that are
Radiology Datasets for AI Model Training
This dataset comprises 1,937,450 DICOM images (totaling 980.24 GB) drawn from 7,279 training studies, 650 public test studies, and 1,517 private test studies
What is Distilled and Quantized Models?
Researchers make advancements in machine learning every day, and I recently explored two of them: distillation and quantization. While I was familiar with
ViT vs CNN: Vision Transformers and Convolutional Neural Networks in Medical Image Analysis
I read this article today and took some notes. I want to mention it here for my future work and to look again. The article is titled: Comparison of Vision
Breast Ultrasound Lesion Classification with PyRadiomics and Scikit-Learn
In the previous post, we explored Radiomics and its Workflow step-by-step. Radiomics is a powerful tool for quantifying imaging features from medical images
Introduction to Radiomics with Python
Radiomics is a field at the intersection of medical imaging and data science that focuses on extracting large quantities of quantitative features from
NVIDIA Holoscan and IGX Orin: Getting Started
I recently had the chance to attend a webinar organized by PNY that focused on industrial and medical edge AI, featuring NVIDIA Holoscan and the NVIDIA IGX
What is Vector Store Optimization?
In the rapidly advancing world of AI, especially with the growth of Large Language Models (LLMs), a new term has emerged for optimizing digital
