Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 2, Convolutional Neural Network

From: https://towardsdatascience.com/only-numpy-medical-denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-2-6def73cabba5 So today, I will continue on the image denoising series, and fortunately I found this paper “Low-dose CT denoising with convolutional neural network. In Biomedical Imagin” by Hu Chen. So lets take a dive into their implementation and see what results we get. Finally, for fun let’s use different type of back propagation to compare … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 2, Convolutional Neural Network

Predicting Invasive Ductal Carcinoma using Convolutional Neural Network (CNN) in Keras

From: https://towardsdatascience.com/predicting-invasive-ductal-carcinoma-using-convolutional-neural-network-cnn-in-keras-debb429de9a6 In this blog, we will learn how to use CNN in a real world histopathology dataset. Real-world data requires a lot more preprocessing than standard datasets such as MNIST, and we will go through the process of making the data ready for classification and then use CNN to classify the images. I will … Continue reading Predicting Invasive Ductal Carcinoma using Convolutional Neural Network (CNN) in Keras

How Data Augmentation Improves your CNN performance? — An Experiment in PyTorch and Torchvision

From: https://medium.com/swlh/how-data-augmentation-improves-your-cnn-performance-an-experiment-in-pytorch-and-torchvision-e5fb36d038fb Simple ways to boost your network performance Credits: https://amanispas.co.za/wp-content/uploads/2019/12/AdobeStock_241822083-Web-Crop-1360x680.jpg In simple terms, Data Augmentation is simply creating fake data. You use the data in the existing train set to create variations of it. This does two things — Increases the size of your training setRegularizes your network The book Deep Learningdefines regularization as any method … Continue reading How Data Augmentation Improves your CNN performance? — An Experiment in PyTorch and Torchvision

Intuitively Understanding Convolutions for Deep Learning

From: https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1 The advent of powerful and versatile deep learning frameworks in recent years has made it possible to implement convolution layers into a deep learning model an extremely simple task, often achievable in a single line of code. However, understanding convolutions, especially for the first time can often feel a bit unnerving, with terms … Continue reading Intuitively Understanding Convolutions for Deep Learning

An Easy Guide to Gauge Equivariant Convolutional Networks

From: https://towardsdatascience.com/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70 Geometric deep learning is a very exciting new field, but its mathematics is slowly drifting into the territory of algebraic topology and theoretical physics. This is especially true for the paper “Gauge Equivariant Convolutional Networks and the Icosahedral CNN” by Cohen et. al.(https://arxiv.org/abs/1902.04615), which I want to explore in this article. The paper uses … Continue reading An Easy Guide to Gauge Equivariant Convolutional Networks