Retinal Lesion Detection With Deep Learning Using Image Patches | Iovs | Arvo Journals
Multidisciplinary Ophthalmic Imaging| January 2018 Retinal Lesion Detection With Deep Learning Using Image Patches Department of Biomedical Data Science, Stanford University, Stanford, California, United States Department of Ophthalmology, Santa Clara Valley Medical Center, San Jose, California, United States Stanford University School of Medicine, Stanford, California, United States Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States Department of Biomedical Data Science, Stanford University, Stanford, California, United States Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States Department of Radiology, Stanford University School of Medicine, Stanford, California, United States Correspondence: Daniel Rubin, Medical School Office Building (MSOB) Room X-335, MC 5464, 1265 Welch Road, Stanford, CA 94305-5479, USA; [email protected] . Investigative Ophthalmology & Visual Science January 2018, Vol.59, 590-596. doi:10.1167/iovs.17-22721 Retinal Lesion Detection With Deep Learning Using Image Patches You will receive an email whenever this article is corrected, updated, or cited in the literature. You can manage this and all other alerts in My Account Carson Lam, Caroline Yu, Laura Huang, Daniel Rubin; Retinal Lesion Detection With Deep Learning Using Image Patches. Invest. Ophthalmol. Vis. Sci. 2018;59(1):590-596. doi: 10.1167/iovs.17-22721. ARVO (1962-2015); The Authors (2016-present) Purpose: To develop an automated method of localizing and discerning multiple types of findings in retinal images using a limited set of training data without hard-coded feature extraction as a step toward generalizing these methods to rare disease detection in which a limited number of Continue reading >>
Github - Seth-park/fundus-diabetes-detection: Kaggle Diabetic Retinopathy Detection Using Theano (lasagne)
Kaggle Diabetic Retinopathy Detection Using Theano (Lasagne) If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The image size was resized to 256x256 and 512x512.For very deep models like the recent Deep Residual Network by MSRA ( ), 256x256 images were used due to GPU memory constraints. For other models, 512x512 images were used. The data labels are highly unbalanced. For a more stable training, oversampling was done to balance the class labels (approximately uniform distribution). Oversampling should be stopped in the later part of the training to avoid overfitting the "rare" classes. 90%/10% stratified random split using sklearn package. Multi-Threaded Data Loading and Realtime Data Augmentation (Multiprocessing) Image batch is prefetched into a queue before being loaded onto GPU.While being prefetched, data is normalized and randomly augmented (rotate, flip, scale, shear) toavoid overfitting. In test time, the images are only rotated randomly. There are two images per patient (left eye / right eye). To take this fact into account, the dense representations of the two eyes after the convolutional layers are concatenated before the last two fully-connected layers. Continue reading >>
Machine Learning For Diabetic Retinopathy Detection
Machine Learning for Diabetic Retinopathy Detection Machine Learning Researcher & Engineer | Kaggle Master or What can modern networks learn from old algorithms I spent last month intensively competing in a Kaggles "Diabetic Retinopathy Detection" challenge . Joined this 4-month long challenge late, having limited access to GPU, I set my goal to implement fast yet powerful framework I learnt from a series of papers publishedby Andrew Ng , Professor at Stanford and his colleagues Adam Coates , PhD and Professor Honglak Lee . I very much enjoyed the competition and particularly the fact that I was able to confirm effectiveness of the approach and finished on 131st position out of 661 teams having made just few submissions. Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. (from the competition description ). The clinical grading process consists of detection certain subtle features, such as microaneurysms, exudates, intra-retinal hemorrhages and sometimes their position relative to each other on images of the eye. OD at presentation with non-proliferative diabetic retinopathy. Thin arrows: hard exudates; Thick arrow: blot intra-retinal hemorrhage; triangle: microaneurysm. Source The competition participants were provided with training and testing sets of high-resolution retina images (37 and 56 thousand respectively) taken under a variety of imaging conditions. A clinician has rated the presence of diabetic retinopathy in each training image on a scale of 0 (no DR) to 4 (proliferative DR). Fundus images indicating the different grades of diabetic Continue reading >>
Recod Code & Data | Recod Reasoning For Complex Data
Authors: Tiago Carvalho, Christian Riess, Elli Angelopoulou, Helio Pedrini, Fabio Faria, Ricardo Torres, and Anderson Rocha. T. J. d. Carvalho, C. Riess, E. Angelopoulou, H. Pedrini and A. d. R. Rocha, Exposing Digital Image Forgeries by Illumination Color Classification, in IEEE Transactions on Information Forensics and Security, vol. 8, no. 7, pp. 1182-1194, July 2013. doi: doi: 10.1109/TIFS.2013.2265677 T. Carvalho, F. A. Faria, H. Pedrini, R. da S. Torres and A. Rocha, Illuminant-Based Transformed Spaces for Image Forensics, in IEEE Transactions on Information Forensics and Security, vol. 11, no. 4, pp. 720-733, April 2016. doi: doi: 10.1109/TIFS.2015.2506548 DSO-1 It is composed of 200 indoor and outdoor images with an image resolution of 2,048 x 1,536 pixels. Out of this set of images, 100 are original, i.e., have no adjustments whatsoever, and 100 are forged. The forgeries were created by adding one or more individuals in a source image that already contained one or more persons. DSI-1 It is composed of 50 images (25 original and 25 doctored) downloaded from different websites in the Internet with different resolutions. Original images were downloaded from Flickr and doctored images were collected from different websites such as Worth 1000, Benetton Group 2011, Planet Hiltron, etc. Authors: Daniel Moreira, Sandra Avila, Mauricio Perez, Daniel Moraes, Vanessa Testoni, Eduardo Valle, Siome Godenstein, Anderson Rocha. Related publication: D. Moreira; S. Avila; M. Perez; D. Moraes; V. Testoni; E. Valle; S. Godenstein; A. Rocha., Pornography Classification: The Hidden Clues in Video Space-Time in Forensic Science International, vol. 268, november 2016, p. 46-61, doi: doi: 10.1016/j.forsciint.2016.09.010 TRoF Temporal Robust Features : Temporal Robust Features (TRoF) Continue reading >>
Github - Hoytak/diabetic-retinopathy-code: Code For The Kaggle Competition
Code for the Kaggle competition . This code uses the ImageMagick convert tool to preprocess the images,then uses the neural net toolkits and boosted tree regression toolkits in Dato'sGraphlab Create package to build the classifier. Use ImageMagick's convert tool to trim off the blank space to thesides of the images, then pad them so that they are all 256x256. Thusthe eye is always centered with edges against the edges of theimage. I avoided scaling the images to improve the neural net performance. Create multiple versions of each image varying by hue and contrastand white balance. Duplicate each class so each class is represented equally, thenshuffle the data. Train several neural nets, one trained to predict the levelmembership, and another 4 to distinguish 0 vs 1-4, 0-1 vs. 2-4, 0-2vs. 3-4, and 0-3 vs. 4. For each image, extract both class predictions and the values of thefinal neural net layer. Pool all of these features across models andvariations of the same images. Train boosted regression trees on these pooled features to predict the level. Round to the nearest integer as the class prediction. Continue reading >>
Nolearn Pypi
Training convolutional neural networks with nolearn For specifics around classes and functions out of the lasagnepackage, such as layers, updates, and nonlinearities, youll want tolook at the Lasagne projects documentation . nolearn.lasagne comes with a number of tests that demonstrate some of the more advanced features, such as networkswith merge layers, and networks with multiple inputs. nolearns own documentation is somewhat out of date at this point. But theres more resourcesonline. Finally, theres a few presentations and examples from around the web.Note that some of these might need a specific version of nolearn andLasange to run: The winner of the 2nd place in the Kaggle Right Whale Recognitionchallenge haspublished his lasagne/nolearn-based code . If youre seeing a bug with nolearn, please submit a bug report to the nolearn issue tracker .Make sure to include information such as: how to reproduce the error: show us how to trigger the bug using aminimal example what versions you are using: include the Git revision and/or versionof nolearn (and possibly Lasagne) that youre using Please also make sure to search the issue tracker to see if your issuehas been encountered before or fixed. If you believe that youre seeing an issue with Lasagne, which is adifferent software project, please use the Lasagne issue tracker instead. Theres currently no user mailing list for nolearn. However, if youhave a question related to Lasagne, you might want to try the Lasagneusers list , or useStack Overflow. Please refrain from contacting the authors fornon-commercial support requests directly; public forums are the rightplace for these. Daniel Nouri. 2014. nolearn: scikit-learn compatible neuralnetwork library See the LICENSE.txt file for license rights andlimitations (MIT). See Gi Continue reading >>
Machine Learning Techniques For Diabetic Macular Edema (dme) Classification On Sd-oct Images
Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024px512px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP\(_{16 {\text{-}} \mathrm{ri}}\) vectors while represented and classified using PCA and Continue reading >>
Using Neural Networks For Diabetic Retinopathy Detection In Eye Images
of Contemporary Informatics, Mathematics and Physics Using Neural Networks for Diabetic Retinopathy Detection in Eye Images Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment (from ). Thus, we will try to develop an automated method for detecting DR in eye images using machine learning techniques in particular we will try Neural Networks approach. First, we will start with implementing simple Softmax classifier, later substituting it with three-layer artificial neural network, ultimate goal is to try to build a convolutional neural network for image classification. In this project we will follow up the Convolutional Neural Networks Course, which is an open online course from Stanford (Classification/regression (hands on some basic classifiers) Cost function optimization (gradient descent) Cross validation techniques (leave-one-out cross validation, ten-fold cross validation) Parameters tuning (grid search/random search) Continue reading >>
Github - Stevenreitsma/kaggle-diabetic-retinopathy: 11th Place Submission For Kaggle's Diabetic Retinopathy Competition
11th place submission for Kaggle's Diabetic Retinopathy Competition If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository contains the work of the AI for an Eye team for Kaggle's Diabetic Retinopathy Detection competition. This README.md file contains some information on how to run the algorithms correctly. python train.py (use train/test splits that are present in the data/processed folder! this is important!) This will train the model and put the model, kappa plot and best weights in the models/
Detecting Diabetic Retinopathy In Eye Images
The past almost four months I have been competing in a Kaggle competition about diabetic retinopathy grading based on high-resolution eye images. In this post I try to reconstruct my progression through the competition; the challenges I had, the things I tried, what worked and what didn’t. This is not meant as a complete documentation but, nevertheless, some more concrete examples can be found at the end and certainly in the code. In the end I finished fifth of the almost 700 competing teams. Update 02/08/2015: Code and models (with parameters) added. Introduction Introduction Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population of the developed world and is estimated to affect over 93 million people. (From the competition description where some more background information can be found.) The grading process consists of recognising very fine details, such as microaneurysms, to some bigger features, such as exudates, and sometimes their position relative to each other on images of the eye. (This is not an exhaustive list, you can look at, for example, the long list of criteria used in the UK to grade DR (pdf).) Some annotated examples from the literature to get an idea of what this really looks like (the medical details/terminology are not very important for the rest of this post): Example of non-proliferative diabetic retinopathy (NPDR): Thin arrows: hard exudates; Thick arrow: blot intra-retinal hemorrhage; Triangle: microaneurysm. (Click on image for source.) Now let’s look at it as someone who simply wants to try to model this problem. You have 35126 images in the training set that look like this annotated by a patient id and “left” or “right” (each patient has two images, one per eye) and divided into 5 fairly unbalanc Continue reading >>
Supervised Retinal Vessel Segmentation From Color Fundus Images Based On Matched Filtering And Adaboost Classifier
Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier Nogol Memari , Conceptualization, Formal analysis, Methodology, Software, Validation, Writing original draft, Writing review & editing,#* Abd Rahman Ramli , Conceptualization, Formal analysis, Methodology, Software, Validation, Writing original draft, Writing review & editing,# M. Iqbal Bin Saripan , Conceptualization, Formal analysis, Methodology, Software, Validation, Writing original draft, Writing review & editing,# Syamsiah Mashohor , Conceptualization, Formal analysis, Methodology, Software, Validation, Writing original draft, Writing review & editing,# and Mehrdad Moghbel , Conceptualization, Formal analysis, Methodology, Software, Validation, Writing original draft, Writing review & editing# Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, Serdang, Selangor, Malaysia Pennsylvania State Hershey College of Medicine, UNITED STATES Received 2017 Jun 23; Accepted 2017 Nov 15. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and t Continue reading >>
Cs584: Deep Learning
Prof. Shamim Nemati (OH: Mon 1:00pm-2:00pm in BMI (36 Eagle Row, 5th Floor South) 579) TF: Supreeth Prajwal (OH: Wed 10:15am-11:15am BMI 581) TF: Ali Ahmadvand (OH: Tue 9am-10am BMI 581) Time: Monday and Wednesday, 11:30am-12:45pm Contact: Instructor firstname dot Instructor lastname at emory.edu Course Website: schedule | assignments | grading | books | faq | January 11, 2017: Please bring your laptops for Lecture 2 (Wed 18 Jan 2017). We will have a hands-on demonstration of various computational resources available at Emory for running large scale deep learning computations. January 23, 2017: Please complete Assignment 1 by Sunday, 01/29/2017. January 30, 2017: Please complete Assignment 2 by Sunday, 02/05/2017. February 8, 2017: Please complete Assignment 3 by Sunday, 02/12/2017. February 16, 2017: Please complete Assignment 4 by Sunday, 02/26/2017. February 20, 2017: Midterm project presentations are on Wed 22 March. Midterm project reports are due Sunday 26th of March. March 7, 2017: Optional Assignment 5 has been posted. March 15, 2017: Please complete Assignment 6 by Wednesday, 03/29/2017. April 3, 2017: Please complete Assignment 7 by Monday, 04/10/2017. April 16, 2017: Please complete Assignment 8 by Monday, 04/23/2017. Continue reading >>
Yerevann.github.io/2015-08-17-diabetic-retinopathy-detection-contest-what-we-did-wrong.md At Master Yerevann/yerevann.github.io Github
After watching the awesome video course by Hugo Larochelle on neural nets (more on this in the [previous post]({% post_url 2015-07-30-getting-started-with-neural-networks %})) we decided to test our knowledge on some computer vision contest. We looked at Kaggle and the only active competition related to computer vision (except for the digit recognizer contest , for which lots of perfect out-of-the-box solutions exist) was the Diabetic retinopathy detection contest . This was probably quite hard to become our very first project, but nevertheless we decided to try. The team included Karen , Tigran , Hrayr , Narek (1st to 3rd year bachelor students) and me (PhD student). Long story short, we finished at the 82nd place out of 661 participants, and in this post I will describe in details what we did and what mistakes we made. All required files are on these 2 github repositories . We hope this will be interesting for those who just start to play with neural networks. Also we hope to get feedback from experts and other participants. Diabetic retinopathy is a disease when the retina of the eye is damaged due to diabetes. It is one of the leading causes of blindness in the world. The contest's aim was to see if computer programs can diagnose the disease automatically from the image of the retina. It seems the winners slightly surpassed the performance of general ophthalmologists. Each eye of the patient can be in one of the 5 levels: from 0 to 4, where 0 corresponds to the healthy state and 4 is the most severe state. Different eyes of the same person can be at different levels (although some contestants managed to leverage the fact that two eyes are not completely independent). Contestants were given 35126 JPEG images of retinas for training (32.5GB), 53576 images for testing Continue reading >>
Nolearn - Pocketcluster Index
Counting 2,581 Big Data & Machine Learning Frameworks, Toolsets, and Examples... scikit-learn compatible neural network library nolearn contains a number of wrappers and abstractions aroundexisting neural network libraries, most notably Lasagne , along with a few machine learningutility modules. All code is written to be compatible with scikit-learn . We recommend using venv (when using Python 3)or virtualenv (Python 2) to install nolearn. To install the latest release of nolearn from the Python PackageIndex, do: At the time of this writing, nolearn works with the latest versions ofits dependencies, such as numpy, scipy, Theano, and Lasagne (thelatter from Git ). But we alsomaintain a list of known good versions of dependencies that we supportand test. Should you run into hairy depdendency issues duringinstallation or runtime, we recommend you try this same set of testeddepdencencies instead: pip install -r install nolearn If you want to install the latest development version of nolearndirectly from Git, run: pip install -r install git+[emailprotected] #egg=nolearn==0.7.git If you're looking for how to use nolearn.lasagne, then there's twointroductory tutorials that you can choose from: Training convolutional neural networks with nolearn For specifics around classes and functions out of the lasagnepackage, such as layers, updates, and nonlinearities, you'll want tolook at the Lasagne project's documentation . nolearn.lasagne comes with a number of tests that demonstrate some of the more advanced features, such as networkswith merge layers, and networks with multiple inputs. nolearn's own documentation is somewhat out of date at this point. But there's more resourcesonline. Finally, there's a few presentations and examples from around the web.Note that some of these mig Continue reading >>
What Happened At The Tensorflow Dev Summit 2017 - Part 1/3: Community & Applications
What happened at the Tensorflow Dev Summit 2017 - Part 1/3: Community & Applications On February 15th 2017, Tensorflow , a software framework for artificial neural networks, version 1.0 was released at the Googles TensorFlow Dev Summit in Mountain View, California. During the event, there were presentations and announcements in four different areas of the project: Community, Applications, Deployment Strategies, Tools & Technology. The event was live streamed, and all the videos are available online . With these series of posts, I will highlight what were the most interesting parts and give practical, hands-on advice on the topics covered during the event. The legendary Jeff Dean kicked off the summit by telling us the story of the project. Tensorflow is not the first attempt of the Google Brain team to create a general Machine Learning framework. The previously closed source project was called DistBelief . Tensorflow was open sourced in November of 2015 and from its public release, there has been an impressive adoption internally in Google and by the community. In little more than a year, the open source Github repository became the number one in the machine learning category with over 44 thousand stars, 500 programmers developed and submitted their software with around a thousands commits per month, and 5,500 independent repositories, with the name Tensorflow in them, are now present on Github. Major universities have started teaching Machine Learning using TensorFlow. Many companies also embraced the project. IBM, Movidius , and Qualcomm are working with Google to fully support hardware acceleration of Tensorflow for their platforms. Travis Lanier from Qualcomm showcased the 8x performances of tensorflows InceptionV3 model running on the Hexagon DSP compared to runni Continue reading >>
