Reduce Dimensions for Single Cell

From: https://towardsdatascience.com/reduce-dimensions-for-single-cell-4224778a2d67 From Becht et al., Nature Biotechnology 2019, image source This is the eighth article in the column Mathematical Statistics and Machine Learning for Life Sciences where I try to cover analytical techniques common for Bioinformatics, Biomedicine, Genetics, Evolutionary Science etc. Today we are going to talk about dimension reduction techniques applied to single cell genomics data. Among others, we are … Continue reading Reduce Dimensions for Single Cell →

Physical Activity Monitoring Using Smartphone Sensors and Machine Learning

From: https://towardsdatascience.com/physical-activity-monitoring-using-smartphone-sensors-and-machine-learning-93f51f4e744a Sitting: One of the Most Dangerous Activities Sedentary behavior has become a major public health risk around the world. Experts tell us that a minimum amount of daily physical activity (PA) is necessary to maintain health and reduce the risk of chronic diseases such as diabetes, heart disease, and cancer. Some researchers have … Continue reading Physical Activity Monitoring Using Smartphone Sensors and Machine Learning →

Retinal Vasculature Segmentation with a U-Net Architecture

From: https://towardsdatascience.com/retinal-vasculature-segmentation-with-a-u-net-architecture-d927674cf57b The structures exhibited by the retinal vasculature infer critical information about a wide range of retinal pathologies such as Prematurity (RoP), Diabetic Retinopathy(DR), Glaucoma, hypertension, and Age-related Macular Degeneration(AMD). These pathologies are amongst the leading causes of blindness. Accurate segmentation of retinal vasculature is important for various ophthalmologic diagnostic and therapeutic procedures. A … Continue reading Retinal Vasculature Segmentation with a U-Net Architecture →

LSTM-FCN for cardiology

From: https://towardsdatascience.com/lstm-fcn-for-cardiology-22af6bbfc27b We will work on the application of the algorithm on a database containing electrocardiograms (ECG) of 2 different types, and see if this new model can help detect patients at a high risk of sudden death. LSTM-FCN architecture LSTM-FCN architecture (source: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8141873) This algorithm consists of 2 parts: a LSTM block and a FCN … Continue reading LSTM-FCN for cardiology →

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 4, Convolutional Residual Neural Networks

From: https://towardsdatascience.com/denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-4-convolutional-resnet-74335714a4ae Another attempt to denoise CT Scan of lungs, this time we are going to use more sophisticated Convolutional ResNet Architecture. Specifically, we are going to use the architecture proposed in this paper, “Deep Residual Learning for Image Recognition”. Also, as usual lets do manual back propagation to compare our results. Network Architecture (Image Form) Image … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 4, Convolutional Residual Neural Networks →

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks

From: https://towardsdatascience.com/denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-3-convolutional-residual-6dbb36b28be So since I will be using a lot of image data, I will move on to Tensorflow to harness the power of GPU however, no worries, we will implement all of our back propagation. (Also compare the final results with auto differentiation). Now due to midterms I wasn’t able to do much, so … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks →

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 →

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 1, Vanilla Auto Encoder Model

From: https://towardsdatascience.com/only-numpy-medical-denosing-lung-ct-scans-using-auto-encoders-with-interactive-code-part-1-a6c3f9400246 Image from Pixel Bay My passion lies in Artificial Intelligent, and I want my legacy to be in the field of Health Care, using AI. So in hopes to make my dream come true as well as to practice OOP approach of implementing neural networks I will start the first part of long series … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 1, Vanilla Auto Encoder Model →

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 →

Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM

From: https://towardsdatascience.com/detecting-heart-arrhythmias-with-deep-learning-in-keras-with-dense-cnn-and-lstm-add337d9e41f Let’s detect abnormal heart beats from a single ECG signal From: https://towardsdatascience.com/detecting-heart-arrhythmias-with-deep-learning-in-keras-with-dense-cnn-and-lstm-add337d9e41f Introduction Recently, I was reviewing Andrew Ng’s team’s work(https://stanfordmlgroup.github.io/projects/ecg/) on heart arrhythmia detector with convolutional neural networks (CNN). I found this quite fascinating especially with the emergence of wearable products (e.g. Apple Watch and portable EKG machines) that are capable of … Continue reading Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM →