Popular GitHub Repositories for Blood Cells

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

AI-powered Jupyter Notebook — use local AI to generate and edit code cells, automatically fix errors, and chat with your data

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

A python package for simulating movement and spatial cell types (e.g. place cells, grid cells) in continuous environments.

Language Modeling

Code for Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, CVPR '19

Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.

Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

Learning cell communication from spatial graphs of cells

A foundation model for universal 3D blood vessel segmentation.

a cutting-edge cell segmentation model specifically designed for single-molecule resolved spatial omics datasets. It addresses the challenge of accurately segmenting individual cells in complex imaging datasets, leveraging a unique approach based on graph neural networks (GNNs).

[ICIP'24 Lecture Presentation] Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".

Source code for predicting Blood Glucose Concentration

GPT JupterLab is a JupyterLab extension to use OpenAI’s GPT models for text and code completion on your notebook cells.

This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease

NABNet: A Nested Attention-guided BiConvLSTM Network for a robust ?prediction of Blood Pressure components from reconstructed Arterial Blood ?Pressure waveforms using PPG and ECG Signals

Repository for the paper "Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations" (2024).