birajaghoshal Goto Github PK

keras-information-dropout

Keras implementation of the Information Dropout (arXiv:1611.01353) paper

keras-uncertainty

Utilities to perform Uncertainty Quantification on Keras Models

keras_sklearn

Combination of Keras CNN with Scikit-learn classifiers.

kerasdropconnect

An implementation of DropConnect Layer in Keras

kwyk

Knowing what you know - Bayesian brain parcellation

ladder

Ladder network is a deep learning algorithm that combines supervised and unsupervised learning

ladder-1

Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.

lal

This project contains code for paper Ksenia Konyushkova, Raphael Sznitman, Pascal Fua 'Learning Active Learning from Data', NIPS 2017

lcbnn

lcvi

Variational Bayesian decision-making for continuous utilities

leukemia-detection-using-cnn-s

Using CNN's on pictures of cells to determine wether the person has Leukemia or not

leveraging-uncertainty-information-from-deep-neural-networks-for-disease-detection

Leveraging uncertainty information from deep neural networks for disease detection

libact

Pool-based active learning in Python

liver-and-pancreatic-carcinoma-classifier

Pancreatic cancer classifier using DNA Methylation information. This project was done as part of our Foundations of Machine Learning course.

lung-diseases-classifier

Diseases Detection from NIH Chest X-ray data

lungnoduledetectionclassification

Automated Extraction and Classification of Pulmonary Lung Nodules from CT Scans

lympht

Track arm angle to aid early detection of lymphedema in breast cancer survivors using openCV

machine-learning

Repository for activities of the machine learning committee

mapie

A scikit-learn-compatible module for estimating prediction intervals.

marugoto

Tools to build deep learning pipelines.

mcbn

medical-data

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

meduncertainty

Uncertainty in Medical Image Analysis

meetup_april_2018

Slides and notes for the 12th of April 2018 meetup.

melanoma-lesion-segmentaion-and-classification

An encoder-decoder CNN (U-Net) for Melanoma lesion segmentation and a standard CNN for classification

mic_locator

midnite

This is a unified interpretability framework for pytorch deep neural networks on visual recognition tasks, consisting of various visualization techniques and uncertainty measures. Please use the latest release of our gitLab version.

mine-mutual-information-neural-estimation-

A pytorch implementation of MINE(Mutual Information Neural Estimation)

misclassificationcostbasedneuralnetworktraining

A neural network is trained by backpropagation of errors which are generated due to difference in actual and predicted class