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jiashi9's Projects

3dunetcnn icon 3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

algorithms icon algorithms

Bug-tracking for Jeff's algorithms book, notes, etc.

course20 icon course20

Deep Learning for Coders, 2020, the website

deepfixcx icon deepfixcx

DeepFix: Explainable Privacy-Preserving Image Compression for Medical Image Analysis

deeplabv3plus icon deeplabv3plus

deeplabv3plus2018:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

gmseg icon gmseg

Spinal cord gray matter segmentation using deep dilated convolutions.

health-lab icon health-lab

Segment the affected part of the brain in the MRI scans along with predicting severity of the alzheimer's disease

java icon java

All Algorithms implemented in Java

keras2kubernetes icon keras2kubernetes

Open source project to deploy Keras Deep Learning models packaged as Docker containers on Kubernetes.

lucid icon lucid

A collection of infrastructure and tools for research in neural network interpretability.

med2image icon med2image

Converts medical images to more displayable formats, e.g. NIfTI to jpg.

medical-image-denoising icon medical-image-denoising

Full Stack Website created for our Final Year Project where CNN-based Image Denoising Model has been created to denoise Medical Images. React Js Library is used for frontend & for the backend part we have used flask,AWS,Linode.

megnet icon megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

niftynet icon niftynet

An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

poxapp icon poxapp

A personalized recommendation system for mpox (monkeypox)

pykale icon pykale

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem

pyradiomics icon pyradiomics

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

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