birajaghoshal Goto Github PK

2018-data-science-bowl

Find the nuclei in divergent images to advance medical discovery

2018-tagm-paper

2018 TAGM paper

3d-unet

A simple implementation of 3D-Unet on a 3D Prostate Segmentation Task

3dunetcnn

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

aaai-22

Repo for "Fair Conformal Predictors for Applications in Medical Imaging" paper

aang_conformal-prediction

Lightweight, useful implementation of conformal prediction on real data.

acquisition_example

Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function

active-learning

Active Learning using Multi Label Image Dataset

active-learning---supervised-machine-learning-with-minimal-labeled-data

Active Learning : Supervised Machine Learning With Minimal Data

active-learning-bayesian-convolutional-neural-networks

Active Learning on Image Data using Bayesian ConvNets

active-learning-framework

A simple framework for Active Learning Experiments

activelearning

activesemisuperviseddeeplearning

Comparing active learning, semi-supervised learning and combination of them on deep learning.

acute-lymphoblastic-leukemia-cell-classification-using-bayesian-convolutional-neural-networks

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.

adae

SIIM/ISIC 2020 Challenge Winning Algorithm (All Data Are Ext)

adventures-in-ml-code

This repository holds all the code for the site http://www.adventuresinmachinelearning.com

adversarial-deep-structural-networks

ISBI2018: Adversarial Deep Structural Networks for Mammographic Mass Segmentation https://arxiv.org/abs/1612.05970

ae_ts

Auto encoder for time series

aif360

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.

aisg_2019

Code used for Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications

anomagram

Interactive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js

applied-deep-learning

Applied Deep Learning Course

ara-cnn

arc

Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems

arch_uncert

Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)

arianna

astronn

Deep Learning for Astronomers with Tensorflow

atten_deep_mil

This is an implementation of ICML 2018 "Attention-based Deep MIL"

avuc

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

awesome-bayesian-deep-learning

A curated list of resources dedicated to bayesian deep learning