This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. Concise overviews are provided of studies per…, ON THE USE OF DEEP LEARNING METHODS ON MEDICAL IMAGES, A Review on Medical Image Analysis with Convolutional Neural Networks, Deep Learning Applications in Medical Image Analysis, Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis, Automatic Analysis of Lesion in Cardiovascular Image using Fully Convolutional Neural Networks, Promises and limitations of deep learning for medical image segmentation, Deep Learning for Cardiac Image Segmentation: A Review, A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches, Applications of Deep Learning to Neuro-Imaging Techniques, Deep Learning in Medical Image Registration: A Review, Deep Neural Networks for Fast Segmentation of 3D Medical Images, Understanding the Mechanisms of Deep Transfer Learning for Medical Images, Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique, Anatomy-specific classification of medical images using deep convolutional nets, Medical Image Description Using Multi-task-loss CNN, Computational mammography using deep neural networks, Deep vessel tracking: A generalized probabilistic approach via deep learning, Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks. IEEE Jour-. 2016b. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. J. M., 2016. A wide variety of applications are addressed: tation of anatomical structures, segmentation and detec-. patches from the 3D-space in a multi-stream fashion, which has been applied by various authors in the con-, Segmentation is a common task in medical image, siﬁcation architectures, they could straightforwardly be, a patch or subimage centered on that pixel or voxel, and, predicting if the pixel or voxel belongs to the object of, that input patches from neighboring pixels have huge, product are both linear operators and they can be rep-, resented interchangeably, which allows the application, of networks to images larger than the ones they were, trained on by rewriting the fully connected layers as, work’ (fCNN) can then be applied as a single set of, stacked convolutions to an entire image in an e, or pooling layers, this may result in output with a far, been proposed to circumvent this resolution degrada-, exactly the same amount of times as the downsampling, factor in each direction with a shift of one pixel each, resolution version of the ﬁnal output, minus the pixels, one step further and proposed an architecture (so-called, U-net architecture), comprising a ’regular’ CNN fol-, lowed by an upsampling part where ’up’-conv, are used to increase the image size, coined contractive, proposed an extension to the U-Net layout that incorpo-, rates ResNet-like residual blocks and a Dice loss layer, that directly minimizes the commonly used segmenta-, One of the main contributors to deep learning taking, ﬂight in recent years has been the cheap and wide avail-, ability of GPU and the corresponding GPU-computing, libraries (CUDA, OpenCL). pp. Hough-CNN: Deep learning for segmentation. the number of weights no longer de-, pends on the size of the input image) that need to be, learned and renders the network equivariant with re-, Convolutional layers are typically alternated with pool-, ing layers where pixel values of neighborhoods are, typically the max or mean operations, which induce a. certain amount of translation invariance. Proceedings of the SPIE. A Survey on Deep Learning methods in Medical Brain Image Analysis Automatic brain segmentation from MR images has become one of the major areas of medical research. Vol. Deep. Vol. A hybrid learning approach for semantic labeling of cardiac, CT slices and recognition of body position. The journal publishes the highest quality, original papers that contribute to the basic science of … convolutional neural network based method for thyroid nodule di-, age quality classiﬁcation using saliency maps and CNNs. NeuroImage 129, end-diastole and end-systole frames via deep temporal regression, C. I., Mann, R., den Heeten, A., Karssemeijer, scale deep learning for computer aided detection of mammo-. Metaxas, D. N., Zhou, X. S., 2016. MRI based prostate cancer detection with high-level, representation and hierarchical classiﬁcation. A., de V, van Ginneken, B., 2016. Computer Methods, in Biomechanics and Biomedical Engineering: Imaging & Visual-. In: Conference Pro-, ceedings of the IEEE Engineering in Medicine and Biology Soci-, sualizing and enhancing a deep learning framework using patients, age and gender for chest X-ray image retrieval. 10008 of Lecture Notes in Com-, BenTaieb, A., Hamarneh, G., 2016. pp. (2016)), segmentation of lesions in the brain (top ranking in BRATS, ISLES and MRBrains challenges, image from Ghafoorian et al. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis. set of around a 1000 images of skin lesions. A CNN, was employed to generate a representation of an image, one label at a time, which was then used to train an. In: IEEE International. In: Medical Image Computing and, 2016b. Trade. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. Table 2: Overview of papers using deep learning techniques for retinal image analysis. ages via independent subspace analysis based hierarchical features. pp. hybrid pretrained and skin-lesion trained layers. Payer, C., Stern, D., Bischof, H., Urschler. Gao, Z., Wang, L., Zhou, L., Zhang, J., 2016e. ages using deep convolutional neural networks. In this section, we introduce the deep learning con-, cepts that are important for and have been applied to, more background can consult one of several revie, sentation of some of the most commonly used networks, Most deep architectures are based on neural networks, and can be considered as a generalization of a linear or, such a network represents a linear combination of some, A neural network consists of several layers, stacked neurons through which a signal is propa-, a way, the model is referred to as a multi-layered percep-, tron (MLP), where the intermediate layers are typically. Biomed. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Download PDF Abstract: Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of … several decades and is a well studied concept. dure. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database. This survey includes over 300 papers, most of them recent, on a wide variety of applications of deep learning in medical image analysis. with unlabeled data. whole slide images. For classification, the trained HMM is used to assign labels to pixels in an MRI image with a dynamic programming approach and the classification result of the image is obtained from the labels assigned to the tumor region. Standard cardiac image post-processing guidelines indicate the importance of the correct identification of a short axis slice range for accurate quantification. It includes all kinds of neural networks including autoencoders, RBM, DBM, and most importantly CNN, as well as different modalities (MRI. G., Sherman, M., Karssemeijer, N., van der Laak, J. phy mass lesion classiﬁcation with convolutional neural networks. 1. able; older scanned screen-ﬁlm data sets are still in use. Multi-atlas segmentation using. One of the earliest papers cov-, ering medical image segmentation with deep learning, algorithms used such a strategy and was published by, tation of membranes in electron microscopy imagery in, window-based classiﬁcation to reduce redundant com-, fCNNs have also been extended to 3D and hav. 699–702. lutional neural networks (CNNs) and recurrent neural, networks (RNNs). Transactions on Image Processing, 968–982. CT colonography. ing for holistic interstitial lung disease pattern detection. Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., Mad-, abhushi, A., Sep 2016. In: Symposium on Biomedical Imaging. histology images. Duration: 8 hours Price: $10,000 for groups of up to 20 (price increase … make their way into medical image analysis. ral networks. The goal of this paper is to propose a new approach to extract speaker characteristics by constructing CNN filters linked to the speaker. Mishra, M., Schmitt, S., Wang, L., Strasser, N., Zischka, H., Peng, T., 2016. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. overviews of studies per application area. artiﬁcial intelligence in the same period. Nature 542, 115–118. pp. multiple organ detection in a pilot study using 4D patient data. 1265–1268. Lo, B., Yang, G.-Z., Jan. 2017. Renal segmentation is one of the most fundamental and challenging tasks in computer aided diagnosis systems. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The competitions, chal-, lenges, and large public datasets that are available for. Discriminating solitary cysts from soft tissue lesions in mammog-, raphy using a pretrained deep convolutional neural network. For a more detailed survey on semantic segmentation methods in medical images we refer the reader to the great work in, ... To overcome this drawback, a technique known as transfer learning has been proposed and applied in many studies. segmentation. DL enables higher level of abstraction and provides better prediction from datasets. In: IEEE. In: Medical Imaging. Classiﬁcation of mass and normal, domain and texture images. posterior-element fractures on spine CT. In-, ternational Journal of Computer Assisted Radiology and Surgery, Bahrami, K., Shi, F., Rekik, I., Shen, D., 2016. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., Leach, M. O., 2013. mentation of vertebral bodies from MR images with 3D CNNs. In: Medical Image Computing and Computer-Assisted, B., van der Laak, J., 2016. IEEE T. tions on Medical Imaging 35 (5), 1252–1262. Two popular architectures trained in such a, way are stacked auto-encoders (SAEs) and deep belief. Acoustic emission (AE) is well known to be an efficient structural health monitoring technique to detect the creation and propagation of micro-cracks within structural materials such as concrete or composites when submitted to quasi-static stresses. Medical Image, Chen, H., Shen, C., Qin, J., Ni, D., Shi, L., Cheng, J. C. Y, A., 2015c. 1414–, J., Comaniciu, D., 2016a. Nascimento, J. C., Carneiro, G., 2016. 9785 of Proceedings of the SPIE. Breast image fea-, malization using sparse autoencoders (StaNoSA): Application to. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. The best systems in LUNA16 still rely on nodule. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. pp. Deep neural networks for fast segmentation of, 3D medical images. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. ics and Biomedical Engineering: Imaging & Visualization, 1–5. proposed in paper An interesting avenue of research, could be the direct training of deep networks for the re-, A variety of image generation and enhancement. Some papers combat this by adapting the loss function: nation of the sensitivity and the speciﬁcity, weight for the speciﬁcity to make it less sensitive to the, ing data augmentation on positive samples (, Thus lesion segmentation sees a mixture of ap-, proaches used in object detection and organ segmenta-, naturally propagate to lesion segmentation as the exist-, Registration (i.e. The structure of this paper is as follows: have been used for medical image analysis. Both auto-encoders take vectorized image patches of, CT and MRI and reconstruct them through four lay-, pervised patch reconstruction they are ﬁne-tuned using, two prediction layers stacked on top of the third layer, of the SAE. In this work, we present advances and future researches, Join ResearchGate to discover and stay up-to-date with the latest research from leading experts in, Access scientific knowledge from anywhere. METHODS graphic lesions. tuning clearly outperformed feature extraction, achiev-, ing 57.6% accuracy in multi-class grade assessment of, machine, c) recurrent neural network, d) convolutional neural network, e) multi-stream convolutional neural network, f) U-net (with a single, outperformed ﬁne-tuning in cytopathology image clas-, ance can be given to which strategy might be most suc-. 115–123. A deep semantic mobile. Experiments have been performed with a gender-dependent corpus (THUYG-20 SRE) under three noise conditions : clean, 9db, and 0db. 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