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Find the nuclei in divergent images to advance medical discovery
2018 TAGM paper
A simple implementation of 3D-Unet on a 3D Prostate Segmentation Task
Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
Repo for "Fair Conformal Predictors for Applications in Medical Imaging" paper
Lightweight, useful implementation of conformal prediction on real data.
Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function
Active Learning using Multi Label Image Dataset
Active Learning : Supervised Machine Learning With Minimal Data
Active Learning on Image Data using Bayesian ConvNets
A simple framework for Active Learning Experiments
Comparing active learning, semi-supervised learning and combination of them on deep learning.
In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.
SIIM/ISIC 2020 Challenge Winning Algorithm (All Data Are Ext)
This repository holds all the code for the site http://www.adventuresinmachinelearning.com
ISBI2018: Adversarial Deep Structural Networks for Mammographic Mass Segmentation https://arxiv.org/abs/1612.05970
Auto encoder for time series
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Code used for Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
Interactive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js
Applied Deep Learning Course
Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems
Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)
Deep Learning for Astronomers with Tensorflow
This is an implementation of ICML 2018 "Attention-based Deep MIL"
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
A curated list of resources dedicated to bayesian deep learning
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.