A survey on deep learning in medical image analysis

This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.

/pdf/3d-u-net-learning-dense-volumetric-segmentation-from-sparse-4yrx1x4zn2.pdf

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

https://spiral.imperial.ac.uk/bitstream/10044/1/53839/13/1-s2.0-S1361841516301839-main.pdf

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

This paper describes the ATLAS experiment as installed in i ts experimental cavern at point 1 at CERN. It also presents a brief overview of the expec ted performance of the detector.

http://absimage.aps.org/image/SES08/MWS_SES08-2008-020010.pdf

The ATLAS Experiment at the CERN Large Hadron Collider

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

/pdf/deep-learning-in-medical-image-analysis-4jwausnkq2.pdf

Deep Learning in Medical Image Analysis

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the tracking detectors makes this task difficult. The current state-of-the-art tools require expert data reduction to convert the data into a fixed low-dimensional form that can be effectively managed by shallow classifiers. We study the application of deep networks to this task, attempting classification at several levels of data, starting from a raw list of tracks. We find that the highest-level lowest-dimensionality expert information sacrifices information needed for classification, that the performance of current state-of-the-art taggers can be matched or slightly exceeded by deep-network-based taggers using only track and vertex information, that classification using only lowest-level highest-dimensionality tracking information remains a difficult task for deep networks, and that adding lower-level track and vertex information to the classifiers provides a significant boost in performance compared to the state of the art.

Jet flavor classification in high-energy physics with deep neural networks

Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks

Yes, apparently they do. Previous research demonstrated that shallow feed-forward nets sometimes can learn the complex functions previously learned by deep nets while using a similar number of parameters as the deep models they mimic. In this paper we investigate if shallow models can learn to mimic the functions learned by deep convolutional models. We experiment with shallow models and models with a varying number of convolutional layers, all trained to mimic a state-of-the-art ensemble of CIFAR10 models. We demonstrate that we are unable to train shallow models to be of comparable accuracy to deep convolutional models. Although the student models do not have to be as deep as the teacher models they mimic, the student models apparently need multiple convolutional layers to learn functions of comparable accuracy.

/pdf/do-deep-convolutional-nets-really-need-to-be-deep-and-5br8ack2ty.pdf

Gregor Urban

Papers

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

Jet flavor classification in high-energy physics with deep neural networks

Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks

Do Deep Convolutional Nets Really Need to be Deep and Convolutional