Popular GitHub Repositories for Ebs Volumes

[ICLR 2024 Oral] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

Blueprint for Ingesting massive volumes of live or archived videos and extract insights for summarization and interactive Q&A

Official Implementation of PVD and PVDAL: http://sk-fun.fun/PVD-AL/

Systematic Literature Review (SLR) using AI involves leveraging artificial intelligence techniques to automate and expedite the process of reviewing and synthesizing large volumes of scholarly literature.

AbdomenAtlas 3.0 (9,262 CT volumes + medical reports). These “superhuman” reports are more accurate, detailed, standardized, and generated faster than traditional human-made reports.

This repo contains Grad-CAM for 3D volumes.

End-to-end Python CT volume preprocessing pipeline to convert raw DICOMs into clean 3D numpy arrays for ML. From paper Draelos et al. "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes."

3D volume-to-volume generative adversarial network for medical image segmentation

This repository contains code for a near real-time news clustering and summarization solution using AWS services like Lambda, Step Functions, Kinesis, and Bedrock. It demonstrates how to efficiently process, embed, cluster, and summarize large volumes of news articles to provide timely insights for financial services and other industries.

Pipelines for generating large volumes of anonymous artificial data that share some of the characteristics of real NHS data

The Covid-19-QBox-Chatbot Model is free pre-compiled chatbot model that addresses typical questions about the COVID-19 virus to help you handle high volumes of questions from your customers, partners and staff. You simply add it to your current chatbot training data.

Bot that addresses typical questions about the COVID-19 virus to help you handle high volumes of questions from your customers, partners and staff.

Mask Propagation and Active Learning in Medical CT Image Segmentation

Accelerated implementation of the SVM algorithm on an embedded SoC with changes to the algorithm structure to accommodate learning from large volumes of data in an incremental manner with approximations to the computations.

Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.

This project demonstrates the creation of a scalable data processing pipeline for handling and analyzing log data from a hypothetical e-commerce platform. Leveraging Hadoop and PySpark, the pipeline is designed to process large volumes of log files, providing meaningful insights into user behavior, system performance, and sales metrics.

This project forecasts monthly healthcare call volumes using time-series modeling (ARIMA), optimizing call center staffing and reducing wait times. Features include trend analysis, model tuning, and an interactive dashboard for real-time insights.

Interactive annotation tool for 3D TIFF volumes with planar views, integrated automatic UNET predictions and live volume rendering for semi-automated semantic binary annotations.

This repository provides a powerful tool for retrieving natural language responses from large volumes of text documents. The system is built using the OpenAI natural language processing framework and is optimized for high-speed performance using the Pinecone.io storage platform.