One or Two Research Paper Digests

In this paper , the authors present a system for generating videos of a talking heard, using a still image of a person and an audio clip containing speech. As per the authors this is the first paper that achieves this without any handcrafted features or post-processing of the output. This is achieved using a temporal GAN with 2 discriminators. Novelties in this paper Talking head video generated from still image and speech audio without any subject dependency. Also no handcrafted audio or visual features are used for training and no post-processing of the output (generate facial features from learned metrics). The model captures the dynamics of the entire face producing natural facial expressions such as eyebrow raises, frowns and blinks. This is due to the Recurrent Neural Network ( RNN ) based generator and sequence discriminator. Ablation study to quantify the effect of each component in the system. Image quality is measured using Model Architecture The model consists...

In this paper , the authors explore the efficiency of lung segmentation, lossless and lossy data augmentation in  computer-aided diagnosis (CADx) of tuberculosis using deep convolutional neural networks applied to a small and not well-balanced Chest X-ray (CXR) dataset. Dataset Shenzhen Hospital (SH) dataset of CXR images was acquired from Shenzhen No. 3 People's Hospital in Shenzhen, China. It contains normal and abnormal CXR images with marks of tuberculosis. Methodology Based on previous literature, attempts to perform training for such small CXR datasets without any pre-processing failed to see good results. So the authors attempted segmenting the lung images before being inputted to the model. This gave demonstrated a more successful training and an increase in prediction accuracy. To perform lung segmentation, i.e. to cut the left and right lung fields from the lung parts in standard CXRs, manually prepared masks were used. The dataset was split into 8:1:1...

In this paper , the authors explore the use of Deep Convolutional Neural Networls (DCNN) in classifying Tuberculosis (TB) in chest radiographs. One of the advantages of deep learning is its ability to excel with high dimensional datasets, such as images, which can be represented at multiple levels.  Dataset Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. DCNN Models and Training AlexNet and GoogLeNet models, including pre-trained (on ImageNet from Caffe Model Zoo) and untrained models were used in the study. It was found that the AUCs of the pretrained networks were greater. The following solver parameters were used for training: 120 epochs; base learning rate for untrained models and for pretrained models, 0.01 and 0.001, respectively with stochastic gradient descent.  Both of the DCNNs in this studied used dropout or model reg...

In this paper , the authors present a deep learning model to detect disease from chest x-ray images. A convolutional neural network (CNN) is trained to detect the disease names. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. CNN Models used and Dataset CNNs encode input images effectively. In this paper, the authors experiment with a Network in Network (NIN) model and GoogLeNet model. The dataset contains 3,955 radiology reports and 7,470 associated chest x-rays. 71% of the dataset accounts for normal cases (no disease). The data set was balanced by augmenting training images by randomly cropping 224x224 images from the original 256x256 size image. Adaptability of Transfer learning Since this boils down to a classification problem on a small dataset, transfer learning is a technique that comes to our mind. The authors experimented this with ImageNet trained models. ImageNet trained CN...

In this paper , the authors propose a method to classify tuberculosis from chest X-ray images using Convolutional Neural Networks (CNN). They achieve a classification accuracy of 85.68%. They attribute the effectiveness of their approach to shuffle sampling with cross-validation while training the network. Methodology Convolutional Neural Network This has been the ultimate tool for researchers and engineers for computer vision tasks. It has been widely used for many general purpose image and video related tasks. There are many great resources to learn about them. I will link a few of them at the end of this post. In this paper, the authors study the famous AlexNet and GoogLeNet architectures in classifying tuberculosis images. A CNN model usually consists of convolutional layers, pooling layers and fully connected layers. Each layer is connected to the previous layers via kernels or filters. A CNN model learns parameters of the kernel to represent global and local features ...

Published in   2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, this paper introduces two main ideas Method noise Non-local (NL) means algorithm to denoise images Method noise It is defined as the difference between the original (noisy) image and its denoised version. Some of the intuitions that can be drawn by analysing method noise are Zero method noise means perfect denoising (complete removal of noise without lose of image data). If a denoising method performed well, the method noise must look like a noise and should contain as little structure as possible from the original image The authors then discuss the method noise properties for different denoising filters. They are derived based on the filter properties. We will not be going in detail for each filter as the properties of the filters are known facts. The paper explains those properties using the intuitions of method noise. NL-means idea Denoised value at...