Deep learning has emerged as the state-of-the-art machine learning method in many applications. With the development of deep learning, computer vision uses a lot of deep learning to deal with various image problems. 2) at the corresponding training sample size and frozen layers, indicating that mammography is an effective auxiliary domain for transfer training to DBT and that malignant and benign masses in DBT are easier to be distinguished by DCNN, similar to that by human vision. The accuracy and usefulness of the labels or annotations obtained from these methods not only depend on the methods used but also how the information is generated and stored in the systems. Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size. We constructed a convolution module based on group normalization method for fast and accurately feature extraction, and an up-sampling module for feature restoring based on nearest neighbor interpolation method. However, it is important to note that augmenting the training set to a certain size is not equivalent to having a set of independent training samples of comparable size. (A) Pathological image with a cancerous tissue region; (B) label region corresponding to the cancerous tissue in (A); (C) pathological image of cancer-free tissue; (D) the effect of magnifying observation of the cancerous tissue in (A); (E) the effect of extracting the original pathological image and the label and extracting it into the diseased tissue; (F) the effect of magnifying observation for a cancer-free image. Without filters we could gather 5028 . 3 shows the dependence of the test AUC on the sample size of the training mammography data. [8] For pattern recognition tasks in images, deep convolutional neural networks (DCNN) are the most commonly used deep learning networks. We further designed a multi-input model called MIFNet to segment the lesions in the pathological image, and increase the dice coefficient to 81.87% in the segmentation of gastric cancer case images, much higher than some existing segmentation models. The encoder gradually reduces the spatial dimension by continuously merging the layers to extract feature information, and the decoder portion gradually restores the target detail and the spatial dimension according to the feature information. The standalone sensitivity of both CAD systems were 25% higher than the radiologists with or without CAD but had an average of more than 2 false positive marks per case. Data augmentation may use techniques such as flipping the image in various directions, translating the image within a range of distance, cropping the image in different ways, rotating the image within a range of angles, scaling the image over a range of factors, generating shape- and intensity-transformed images by linear or non-linear methods. The current deep learning technology has achieved research results in the field of ultrasound imaging such as breast cancer, cardiovascular and carotid arteries. The increasing workload makes it difficult for radiologists and physicians to maintain workflow efficiency while utilizing all the available imaging information to improve accuracy and patient care. The study cohort contained over 110,000 screening examinations in each group. Lin M, Chen Q, Yan S. Network in network. Finally, FCN uses the deconvolution and fusing feature maps to restore the image, and provides the segmentation result of each pixel by softmax. The test period will serve both as a real world evaluation of the CAD tool on the local population and user training. Other than detection and characterization of abnormalities, applications such as pre-screening and triaging, cancer staging, treatment response assessment, recurrence monitoring, and prognosis or survival prediction are being explored. 8600 Rockville Pike CAD or AI is expected to be useful decision support tools in medicine in the near future. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). According to relevant data statistics, the detection rate of pulmonary nodule has increased 5 times in recent years. Figure 8 shows our left ventricular segmentation effect. Numerous studies have reported promising results. HHS Vulnerability Disclosure, Help They found that arbitration was performed in 1.3% of the cases in single reading with CAD. This example illustrates that, although the total number of images appeared to be large, the lack of high quality labeling may reduce its effectiveness in deep learning training. SegNets encoder and decoder each have 13 convolution layers. Then we detailed the application of deep learning in the classification and segmentation of medical images, including fundus, CT/MRI tomography, ultrasound and digital pathology based on different imaging techniques. AlexNets error rate in ImageNet was 15.3%, which was much higher than the 26.2% in second place. [41] conducted a meta-analysis of clinical studies comparing single reading with CAD or double reading to single reading alone. Chan H-P, Doi K, Galhotra S, Vyborny CJ, MacMahon H, Jokich PM. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. The validation set may be split from the training set by cross validation or by hold-out. [48] showed that the learned features in the shallow layers are more generic, whereas the learned features in the deeper layers become increasingly specific to the task that the DCNN is being trained for. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. For off-line augmentation, the augmented versions of the images are pre-generated and mixed with the original data into a larger training set, which is randomly grouped as mini-batches for the DCNN training. To date, the largest annotated public data set available is the ImageNet data, which contained photographic images containing over 1000 classes of everyday life objects such as animals, vehicles, plants, ships, planes, etc. One of the key factors for the development and its proper clinical adoption in medicine would be a good mutual understanding of the AI technology, and the most . Krizhevsky A, Sutskever I, Hinton GE. We reduce the initial learning rate of 0.0001 by a factor of 10 if . The potential of applying deep-learning-based medical image analysis to computer-aided diagnosis (CAD), thus providing decision support to clinicians and improving the accuracy and efficiency of various diagnostic and treatment processes, has spurred new research and development efforts in CAD. government site. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Baumgartner CF, Kamnitsas K, Matthew J, et al. The reason may be that MRI imaging itself is a three-dimensional form, so the use of three-dimensional convolution can better interpret the segmented object. There could be many kinds of applications of deep learning technology in medical imaging to enhance the burden of medical doctors, quality of healthcare system and patient outcomes. With the development of deep learning technology, a series of deep learning methods are emerging to detect pulmonary nodules. A data set of 260 DBT cases including 65 cancer, 65 benign, and 130 normal cases were read by 24 radiologists. Handwritten digit recognition with a back-propagation network, Proc Advances in Neural Information Processing Systems, Computer-assisted diagnosis of lung nodule detection using artificial convolution neural network. AlexNet demonstrated the pattern recognition capability of the multiple layers of a deep structure. In the field of gastric cancer pathology our research team has established a benign and malignant diagnostic system based on gastric cancer pathology based on deep learning, with a sensitivity of over 97% (47). (39) developed the FastVentricle architecture based on the ENet architecture. CAD systems were therefore approved by FDA for use as a second opinion but not as a primary reader or pre-screener. The acceptance testing or preclinical testing described above can serve as the baseline performance on the local population. In the CADET II study by Gilbert et al. and transmitted securely. It is well known in machine learning that the training or validation performance is generally optimistically biased [5661]. They might have over-relied on the CAD marks and thus did not maintain their vigilance in searching for lesions while increasing their recalls. FCN, fully convolutional network. Application of multi-modality imaging further increases the amount of image data to be interpreted. None of the changes were statistically significant. U-Net adopts the skip connection strategy of splicing to make full use of the features of the downsampling part of the encoder to be used for upsampling. et al. The collection of normal and abnormal cases with special imaging modalities such as MR or PET is even more challenging because a relatively small number of patients will have these examinations and the availability may depend on the protocols for different types of diseases in different health systems. [30] proposed residual learning and showed that a residual network (ResNet) with 110 to 152 layers could outperform several other DCNNs and won the ILSVRC in 2015. arXiv preprint arXiv:1312.4400, 2013. C1 denotes the first convolutional layer was frozen, C1-Ci (i=2, 3, 4, 5) denotes the C1 to Ci convolutional layers were frozen during transfer training. Its a dense structure with a small number of convolution kernels of each size, and use 11 convolutional layer to reduce the amount of computation. Correspondence: Heang-Ping Chan, Ph.D., Department of Radiology, University of Michigan, 1500 E. Medical Center Drive, Med Inn Bldg C477, Ann Arbor, MI 48109-5842, The publisher's final edited version of this article is available at, Machine learning, deep learning, artificial intelligence, computer-aided diagnosis, medical imaging, big data, transfer learning, validation, quality assurance, interpretable AI. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Automation will be useful but it may require the development of an intelligent data mining tool. et al. DCNN therefore discovers feature representations through training and does not require manually designed features as input. With the development of deep learning technology, some segmentation networks based on convolution structure are derived. AlexNet has over 60 million weights and the ImageNet data set for training includes over 1.2 million images with annotations. The data point and the upper and lower range show the mean and standard deviation of the test AUC resulting from ten random samplings of the training set of a given size from the original set. The ROI-based AUC performance for classifying the 9,120 DBT training ROIs (serve as a test set at this stage) for three transfer networks at Stage 1. In recent years, various types of medical image processing and recognition have adopted deep learning methods, including fundus images, endoscopic images, CT/MRI images, ultrasound images, pathological images, etc. Conventional machine learning approach to CAD in medical imaging used image analysis methods to recognize disease patterns and distinguish different classes of structures on images, e.g., normal or abnormal, malignant or benign. Advances in neural information processing systems, Proc Advances in neural information processing systems (NIPS14). The key factors of image processing in medical imaging field are imaging speed, image size and resolution. Shalev-Shwartz et al. Codella NCF, Nguyen QB, Pankanti S, et al. first introduced CNN to the analysis of medical images in 1993 and trained a CNN for lung nodule detection in chest radiographs [11, 12]. Articles & Issues. One of the major reasons may be that CAD tools developed with conventional machine learning methods may not have reached the high performance that can meet physicians needs to improve both diagnostic accuracy and workflow efficiency. The discussions have not attracted much attention, probably because of the limited use of CAD in the clinic at that time. The increase in recall rate for double reading without arbitration was more than twice of that for single reading with CAD. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. The deep learning model relies heavily on data sets. Segmentation results of GNNI U-net on Sunnybrook dataset (A) and LVSC dataset (B). Deep learning for medical image analysis and CAD. The first CAD commercial system was approved by the Food and Drug Administration (FDA) for use as a second opinion in screening mammography in 1998. They appeared to show that after radiologists used CAD in the clinic for a period of time, the many false positive CAD marks they have seen may have desensitized their attention and most of the marks were dismissed including true positives. INTRODUCTION Since its official declaration on March 12, 2020, the coronavirus disease 2019 (COVID-19) pandemic has been an unprecedented global public health crisis, has put health-care organizations worldwide into a state of emergency, and has had enormous socioeconomic impact. Zech J, Pain M, Titano J, Badgeley M, Schefflein J, Su A Data augmentation can be implemented on-line or off-line and an augmentation operation in a specified range can be performed randomly or by fixed increments. (reprint with permission [49]). 2016:2818-26. Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm.2020.02.44). Historical overview In: Li Q, Nishikawa RM, editors. Reconstruction, segmentation, and analysis of medical images. Like the CPU, the GPU is a processor in the graphics card that designed to perform complex mathematical and geometric calculations that are required for graphics rendering. Edited by Mattias Heinrich, Marleen de Bruijne . The enthusiasm has spurred numerous studies and publications in CAD using deep learning. Qaiser T, Mukherjee A, Reddy Pb C, et al. It is a method of data processing using multiple layers of complex structures or multiple processing layers composed of multiple nonlinear transformations (1). B. In fact, similar impact is happening in domains like text, voice, etc. In: International Conference on Functional Imaging and Modeling of the Heart. Image processed by the ACE algorithm. (reprint with permission [49]). In multiple problems, algorithms based on deep learning technologies have achieved unprecedented performance and set the state-of-art. The area under the receiver operating characteristic curve (AUC) for the test ROIs was plot as box-and-whisker plots of 10 repeated experiments under each condition. DCNN is considered a feature extractor that learns representation of the input data by extracting multiple levels of abstractions by its convolutional layers. The choice between off-line and on-line augmentation may depend on the tradeoffs between computational resources and storage space or memory; off-line augmentation is more practical if the available training set is small as it requires more space and memory for the augmented set, while on-line augmentation is preferred for large training sets if computational resource is plentiful. SegNet (17) is a depth semantic segmentation network designed by Cambridge to solve autonomous driving or intelligent robots, which is also based on the encoder-decoder structure. Heidelberg: Springer, 2013:466-73. Review for deep learning based on medical imaging diagnosis. Going deeper with convolutions. . 13.828 Impact Factor. MIDL has a broad scope, including all areas of medical image analysis and computer-assisted intervention, where deep learning is a key element. Deep learning is one of the powerful tools for medical image analysis. Lieman-Sifry J, Le M, Lau F, et al. Regardless of the methods, the validation set is a part of the training process because it is repeatedly used to guide training, and the model structure and parameters are usually chosen to maximize the performance on the validation set. Brain MRI analysis is mainly for the segmentation of different brain regions and the diagnosis of brain diseases, such as brain tumor segmentation (31), schizophrenia diagnosis, early diagnosis of Parkinsons syndrome (32) and early diagnosis of AD. Arbitration was used in cases of recall due to the second reader or CAD. et al. The accuracy rate of common X-ray chest film in the diagnosis of pulmonary nodules is less than 50%, and even people with normal chest film can be detected to infer sarcoidosis. A fully automated system for screening mammograms. Explainable and Generalizable Deep Learning Methods for Medical Image Computing. Most of the DCNN models in medical imaging were trained by transfer learning using models initialized with ImageNet-pretrained weights and fine-tuned by limited medical image data. Poudel RPK, Lamata P, Montana G. Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. et al. SegNet records the element position information of the maximum pooling operation when the encoder is downsampled, and restores the image according to the position information when sampling on the decoder. On the other hand, the application of CNN model in medical image analysis has become one of the most attractive directions of deep learning. A Survey on Deep Learning in Medical Image Analysis. Learning Deep Features for Discriminative Localization. In the field of deep learning, image classification and its application have made great progress this year. The users should familiarize themselves with the output of the CAD tool and quantitatively, if possible, assess the performance of the CAD tool on a large number of consecutive clinical cases. After five rounds of convolution and pooling operations, the 66256 feature matrix finally sent to the fully connected layer. Chan H-P, Sahiner B, Lo SCB, Helvie MA, Petrick N, Adler DD In the field of ultrasound imaging of breast nodules, Chen et al. Sahiner B, Chan H-P, Petrick N, Wagner RF, Hadjiiski LM. Semmlow JL, Shadagopappan A, Ackerman LV, Hand W, Alcorn FS. The side-output layer provides early classification result. government site. A small DBT set was then used for a second-stage transfer training to the target task. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network, A Fast Learning Algorithm for Deep Belief Nets. The data point and the upper and lower range show the mean and standard deviation of the test AUC resulting from ten random samplings of the training set of a given size from the original set. Not all rare diseases can be predicted in this way, which brings new challenges and opportunities for the diagnosis of intractable diseases. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. How to apply big medical data to clinical practice? The recall rate was 10.6% for single reading with CAD, slightly higher than the recall rate of single reading alone (10.2%) but lower than that of double reading (11.9%). Payan et al. In August 1999, NVIDIA released a GeForce 256 graphics chip codenamed NV10. As CAD/AI tools are anticipated to have widespread use in health care in the future, either as second opinion or automated decision maker in some applications such as pre-screening or triaging, their impact on patient care or welfare can be much greater. Literature search for publications in peer-reviewed journals by Web of Science from 1900 to 2019 using key words: ((imaging OR images) AND (medical OR diagnostic)) AND (machine learning OR deep learning OR neural network OR deep neural network OR convolutional neural network OR computer aid OR computer assist OR computer-aided diagnosis OR automated detection OR computerized detection OR Computer-aided detection OR automated classification OR computerized classification OR decision support OR radiomic) NOT (pathology OR slide OR genomics OR molecule OR genetic OR cell OR protein OR review OR survey)). In the field of breast cancer pathology, Qaiser et al. The conventional machine-learning-based CAD for detection of breast cancer in screening mammography is the only CAD application in widespread clinical use to date. Big medical data, deep learning, classification, segmentation, object detection. The Figure 3 shows our classification model of fundus. Fig. Third, when too many layers are frozen during transfer learning, the performance of the DCNN after two-stage training may not reach the same level as that of the DCNN with less layers frozen using the same training sample sizes (compare curves B and C in Fig. Early clinical trials [39, 40] to compare single reading with CAD to double reading showed promising results. 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.