Other applications included segmentation and classification of organs and tumors of different types, classification of changes in tumor size or texture for assessment of treatment response or prediction of prognosis or recurrence. Although there were no systematic studies of how CAD was used in the clinic, Fenton et al. Samala RK, Chan H-P, Hadjiiski LM, Helvie MA, Cha KH, Richter CD. Rectified linear units improve restricted boltzmann machines. 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. 5). At the same time, it supports heterogeneous distributed computing, which can run on multiple GPUs at the same time, and can automatically run the model on different platforms. C. The other issue with human interpretation is that it is limited and prone to errors due to various factors . arXiv preprint arXiv:1502.02506, 2015. Matched studies: the assessment before and after using CAD was on the same mammograms. A small DBT set was then used for a second-stage transfer training to the target task. With the development of deep learning technology, a series of deep learning methods are emerging to detect pulmonary nodules. C. Currently FDA has no post-market monitoring and regulations on the consistency or accuracy of CAD software as second opinion in clinical use after it is approved and there is no control of off-label use. The results are summarized in Figure 2 to Figure 5. Federal government websites often end in .gov or .mil. Figure 2 shows the main medical application scenarios of deep learning. Different from the fusion operation of the direct addition feature when the FCN is upsampled, the U-Net upsampling process first uses the concatenate operation to splicing the feature maps before the up-sampling of the encoder and the downsampling of the decoder. On the one hand, the academic circles have made great efforts to design a variety of efficient CNN models, which have achieved high accuracy and even exceeded the human recognition ability. The .gov means its official. 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 authors acknowledged that Prior reports have confirmed that not all cancers are marked by CAD and that cancers are overlooked more often if CAD fails to mark a visible lesion and that CAD might improve mammography performance when appropriate training is provided on how to use it to enhance performance. A fully automated system for screening mammograms. They demonstrated that reading with CAD could provide all the benefits a radiologist would hope for: reducing the average reading time by more than 50% for a DBT case, increasing sensitivity and specificity, as well as reducing recall rate. The main method for studying related fundus diseases using deep learning techniques is to classify and detect fundus images, such as diabetic retinopathy detection and glaucoma detection. In: International Conference on Functional Imaging and Modeling of the Heart. Deep learning has developed into a hot research field, and there are dozens of algorithms, each with its own advantages and disadvantages. Reconstruction, segmentation, and analysis of medical images. 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. They found that arbitration was performed in 1.3% of the cases in single reading with CAD. Very deep convolutional networks for large-scale image recognition. About. The full name of the GPU is the Graphics Processing Unit, a microprocessor that performs image computing on PCs, workstations, game consoles and some mobile devices. Chan et al. government site. Segmentation results of GNNI U-net on Sunnybrook dataset (A) and LVSC dataset (B). Constructing matrix for pixels based on feature vectors and the manual labels. At present, deep learning technology is mainly used in classification and segmentation in medical images. Improvement in radiologists detection of clustered microcalcifications on mammograms. Due to the limitation of the hardware, the processing of medical images calculated according to sequence. With the success of deep learning in many machine learning applications such as text and speech recognition, face recognition, autonomous vehicles, chess and Go game, in the past several years, there are high expectations that deep learning will bring breakthrough in CAD performance and widespread use of deep-learning-based CAD, or artificial intelligence (AI), to various tasks in the patient care process. As summarized in Table 2, they found that the changes in the radiologists sensitivity or specificity with CAD were only 1% to 2%. Historical overview In: Li Q, Nishikawa RM, editors. Secondly, as the number of network layers increases, the accuracy rate is not very large, which indicates that the deep learning algorithm itself has its limitations. by Beth Miller, Washington University in St. Louis. FastVentricle: cardiac segmentation with ENet. Going deeper with convolutions. The original training set is input in mini-batches but each image in a batch is randomly altered according to the pre-selected probability and range of the augmentation techniques. The FastVentricle architecture is a FCN architecture for ventricular partitioning that runs four times faster than the best ventricular partitioning structure and six times less memory. Amid the high expectations of the accuracy and efficiency that AI can bring to medicine, many challenges have yet to be overcome in order to integrate the new generation of CAD tools into clinical practice and to minimize the risk of unintended harm to patients. Lieman-Sifry et al. Numerous studies have reported promising results. Deep learning ensembles for melanoma recognition in dermoscopy images. Lo SCB, Chan H-P, Lin JS, Li H, Freedman M, Mun SK. Pathological image classification of gastric cancer based on depth learning. Analysis of ultrasound and pathology images for special types of breast malignant tumors. It is also due to the lack of computing resources that the processing of these images wastes a lot of valuable time of doctors and patients. More importantly, workflow efficiency and costs are major considerations in health care. [. Taylor et al. The American Association of Physicists in Medicine (AAPM) CAD Subcommittee (renamed as Computer-Aided Image Analysis Subcommittee in 2018) has published an opinion paper to discuss the training and evaluation methodology for development of CAD systems [62]. The experiences of the single readers in the CAD arm were matched to those of the first readers in the double reading arm. the contents by NLM or the National Institutes of Health. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Payan et al. Not all rare diseases can be predicted in this way, which brings new challenges and opportunities for the diagnosis of intractable diseases. Cardiac MRI analysis diagnoses heart disease by dividing the left ventricle to measure left ventricular volume, ejection fraction, and wall thickness. Fauw JD, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S arXiv preprint arXiv:1502.03167, 2015. Keywords 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 . Its application mainly includes early tumor screening and benign and malignant diagnosis of tumor. The hippocampus segmentation result. Odds ratios (95% confidence interval) of increase in cancer detection rate and increase in recall rate obtained by comparison of single reading with CAD and double reading to single reading alone by Taylor et al.[41]. Although one can expect that the training sample size required for transfer training for a given task will depend on many factors such as the complexity of the tasks and the DCNN structure, the differences in the characteristics between the source and the target domains, the relative training sample sizes between the tasks, the relative trends observed from this study will likely be applicable to many transfer learning applications, and multi-stage transfer training with data from similar domains should be helpful if the training data of the target domain is too scarce. U-Net (16) was proposed by Olaf based on FCN, and has been widely used in medical imaging. Fig. In order to fuse the context information under multi-scale at the same level, PSPNet (18) proposes a pooled pyramid structure, which realizes image segmentation in which the target environment can be understood, and solves the problem that FCN cannot effectively deal with, the relationship problem between global information and scenes. 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)). Histopathological image analysis: a review. Huo ZM, Summers RM, Paquerault S, Lo J, Hoffmeister J, Armato SG Since Krizhevsky et al. Conventional machine learning approach has limitations in that the human developer may not be able to translate the complex disease patterns into a finite number of feature descriptors even if they have seen a large number of cases from the patient population. Received 2019 Sep 16; Accepted 2020 Feb 6. Clinically applicable deep learning for diagnosis and referral in retinal disease, Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, Computer-Aided Diagnosis in the Era of Deep Learning. To generate reference standards for CAD development, one needs to correlate the imaging and clinical test data with outcomes at the various stages. Computer-Aided Detection and Diagnosis in Medical Imaging. Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H. Understanding Neural Networks Through Deep Visualization. The experimental concurrent CAD had a case-based sensitivity of over 90% and a specificity of over 40%, which are higher than all of the CAD tools currently used in screening DM. C0 denotes no layer was frozen, i.e., the pretrained weights in all layers were allowed to be updated. They conducted an observer study to compare single reading with and without CAD using two commercial CAD systems applied to 300 screening cases (150 cancers and 150 benign or normal) from the Digital Mammographic Imaging Screening Trial (DMIST). give ELM the matrix and get the binary retinal vascular segmentation, Average accuracy 0.9607; sensitivity 0.7140; specificity 0.9868, Multiscale CNN + CRF + improved cross-entropy loss, Combining with CNN and fully CRFs. The enthusiasm has spurred numerous studies and publications in CAD using deep learning. Although these early CNNs were not very deep, the pattern recognition capability of CNN in medical images were demonstrated. et al. By analyzing genomics, pathomics, imaging, and other biological data with computers, mathematical modeling, and applying it to clinical and scientific research, ML is a method . They reported that the average sensitivity decreased by 2.3% and the recall rate increased by 4.5% with the use of CAD. 2016:2818-26. Among them, the broadest field of deep learning applications is the early diagnosis of AD. The performance of the developed CAD system is often limited in its discriminative power or generalizability, resulting in high false positive rate at high sensitivity or vice versa. In fact, similar impact is happening in domains like text, voice, etc. Some users might have misunderstood the limitations and performance of the CAD systems. Winsberg F, Elkin M, Macy J, Bordaz V, Weymouth W. Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis, Kimme C, OLaughlin BJ, Sklansky J. Recognition of mammographic microcalcifications with artificial neural network. For patient cases that have been transferred between different hospitals, the incomplete prior or follow-up information may introduce errors into data curation. HHS Vulnerability Disclosure, Help et al. (34) proposed a 3D CNN for AD diagnosis based on SAE pre-training. Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening, Radiological Society of North America Scientific Assembly and Annual Meeting, Improving Workflow Efficiency for Mammography Using Machine Learning. The study by Cole et al. and transmitted securely. With the development of deep learning, computer vision uses a lot of deep learning to deal with various image problems. However, they have significant limitations that make clinicians remain skeptical when applied to clinical practice. The screening mammograms of each patient were independently read in two arms; one was single reading with CAD and the other was their standard practice of double reading. Based on the idea of FCN deconvolution to restore image size and feature, U-Net constructs the encoder-decoder structure in the field of semantic segmentation. A decision support tool will not be acceptable if it requires additional time and/or costs without significant clinical benefits. They reported that the sensitivity of single reading with CAD was 90.4%, higher than the sensitivities of either single reading alone (81.4%) or double reading (88.0%). Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Using deep learning for image classification is earliest rise and it also a subject of prosperity. 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. . The hand-engineered features may also have difficulty to be robust against the large variations of normal and abnormal patterns in the population. Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Hoffmeister JW He K, Zhang X, Ren S, et al. In recent years, GPU has made great progress and moved towards the direction of general computing. CAD systems are developed with machine learning methods. 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. In this chapter, we will discuss some of these issues and efforts needed to develop robust deep-learning-based CAD tools and integrate these tools into the clinical workflow, thereby advancing towards the goal of providing reliable intelligent aids for patient care. An official website of the United States government. The overall recall rate therefore increased in the single reading with CAD from 3.4% to 3.9%. This is an issue of great concern to medical and computer researchers, and intelligent imaging and deep learning provide a good answer. The upsampling part of the decoder uses UnPooling. Deep learning application in medical image analysis. Codella NCF, Nguyen QB, Pankanti S, et al. Chinese Journal of Medical Imaging 2015;(3):188-91. In: Proceedings of the IEEE conference on computer vision and pattern recognition. et al. Cha KH, Petrick N, Pezeshk A, Graff CG, Sharma D, Badal A BioRxiv 2016. doi: https://doi.org/ 10.1101/070441. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH Baumgartner CF, Kamnitsas K, Matthew J, et al. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Automated detection of microcalcifications in mammography. Heidelberg: Springer, 2013:466-73. Review for deep learning based on medical imaging diagnosis. CAD systems are developed with machine learning methods. Big databases have to be collected to provide sufficient training and validation samples to develop robust deep learning models and independent testing with internal and external multi-institutional data to assess generalizability; performance standards, acceptance testing, and quality assurance procedures should be established for each type of applications to ensure the performance of a deep learning model can meet the requirements in the local clinical environment and remains consistent over time; adequate user training in local patient population is vital to allow users to understand the capability and limitations of the CAD tool, establish realistic expectations and avoid improper use or disillusion; CAD recommendation has to be interpretable to allow clinicians to make informed decisions. As a result, a deep learning algorithm well trained and independently tested showing high accuracy using data collected from the same site(s) may not be generalizable to different clinical sites that may have different population or imaging characteristics. Other than the differences in the study designs and radiologists experiences in the studies, the variations may also be attributed to the varied ways that radiologists used CAD in the clinic. 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. Early clinical trials [39, 40] to compare single reading with CAD to double reading showed promising results. In the field of deep learning, image classification and its application have made great progress this year. A second radiologist was consulted for 2.1% of the cases interpreted by single reading with CAD but the consult might or might not be related to CAD marks. DCNN is considered a feature extractor that learns representation of the input data by extracting multiple levels of abstractions by its convolutional layers. The test AUC increased steadily as the training sample size increased. (A) shows the lung image; (B) represents the position of pulmonary nodule in the lung image. With 10-fold cross validation, they showed that the DCNN could identify 34% and 91% of the normal mammograms at a negative predictive value (NPV) of 0.99 for a cancer prevalence of 15% and 1%, respectively. (C,D) is also similar. For an AI model to be a useful routine clinical tool, it is crucial to validate that its performance in clinical settings can meet certain standards and is consistent over time, similar to other medical devices, especially for any AI model that is designed to operate as a decision maker, rather than as a decision support tool or a second opinion. This work is supported by National Institutes of Health award number R01 CA214981. Image classification using artificial neural networks, Detection of masses on mammograms using a convolution neural network, Proceedings of International Conference on Acoustics, Speech and Signal Processing, Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images. (38) designed a detector combined with a neural network classifier to detect the ROI containing LV. How to better apply deep learning to all stages of medical treatment becomes a more challenging task. Cham: Springer, 2017:120-9. et al. Stochastic dual coordinate ascent methods for regularized loss minimization. Caffe features high-performance, seamless switching between CPU and GPU modes, and cross-platform support for Windows, Linux and Mac. 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. Lo et al. The whole picture of pulmonary nodule. Although transfer learning can alleviate the problem of limited data to a certain degree, a large training set is still needed to achieve a high performance DCNN model for a given target task. For example, deep learning models are essentially black boxes that do not offer explainability of their decision-making process which in . 2. Chan H-P, Doi K, Galhotra S, Vyborny CJ, MacMahon H, Jokich PM. Deep learning has been applied to many medical image analysis tasks for CAD [3234]. Qaiser T, Mukherjee A, Reddy Pb C, et al. Its architecture is very different from that of the CPU. The DCNN learns multiple levels of feature representations from the input data by using the deep architecture of convolution layers. Figure 6 shows the segmented result of these networks. These results were very different from those observed in the early days of CAD development when radiologists were enthusiastic about CAD. Dependence of test AUC on mammography training sample size using strategy (A) transfer training. This study is a literature review used to look into the details of some articles, focusing on the uses of DL in different medical tasks of TB that we can extract from chest imaging. Computer-aided diagnosis in mammography: Detection of masses by artificial neural network. Artificial Convolution neural network for medical image pattern recognition. The deep learning method simulates the human neural network. Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography. et al. applied a similar shift-invariant neural network for the detection of clusters of microcalcifications in 1994 [19]. Chan H-P, Doi K, Vyborny CJ, Schmidt RA, Metz CE, Lam KL The test AUC was obtained by applying the AlexNet transfer-trained with mammography data directly to classify the masses on DBT without the second-stage fine-tuning with DBT. The effect of different number of layers of the DCNN being frozen during transfer learning of ImageNet-pretrained AlexNet to classify malignant and benign masses on mammograms. Pytorch is the python version of torch, a neural network framework that is open sourced by Facebook and specifically targeted at GPU-accelerated deep neural network programming. Poudel et al. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M Medical imaging is a very important part of medical data. CNN, convolutional neural network; FCN, fully convolutional network. FCN, fully convolutional network. Deep-learning-based CAD or AI follows similar general principles as conventional machine learning methods, and the need for independent testing will be even more important due to the vast capacity of deep learning to extract and memorize information from the training set. Stage 1 (MAM:C1) denotes single stage training using mammography data and the C1-layer frozen during transfer learning without stage 2. Stage 2 (DBT:C1) denotes Stage 2 C1-frozen transfer learning after Stage 1 transfer learning with a fixed (100%) mammography training set. A recent observer study [64] of breast cancer detection in DBT by radiologist alone in comparison to using deep-learning-based CAD as a concurrent reader that marked suspected lesions and showed the confidence of malignancy on the DBT slices. Deep learning on the medical imaging applications is not limited to the detection of big data routine diseases, but also effective solutions for rare diseases. This study indicated that the specificity of a decision support tool has to be high to avoid inducing fatigue on clinicians response to the computers recommendations. The effectiveness of the feature descriptors often depends on the domain expertise of the CAD developers and the capability of the mathematical formulations or empirical image analysis techniques that are designed to translate the image characteristics to numerical values. However, there are certain limitations in migration learning. Provenance and Peer Review: This article was commissioned by the Guest Editors (Haotian Lin and Limin Yu) for the series Medical Artificial Intelligent Research published in Annals of Translational Medicine. TensorFlows components are excellent, and it provides powerful visualization capabilities through TensorBoard, which can generate very powerful visual representations of real-world network topologies and performance. [42] noted that radiologists with variable experience and expertise may use CAD in a nonstandardized idiosyncratic fashion, and Some community radiologists, for example, may decide not to recall women because of the absence of CAD marks on otherwise suspicious lesions.