medical imaging with deep learning impact factor

The performance of the FCN on the Pascal VOC 2012 datasets (15) has increased by 20% compared to the previous method, reaching 62.2% of the mIOU. Their mammography set contained 2242 unique views (craniocaudal or mediolateral oblique) with 2454 regions of interest (ROIs) containing breast masses. An official website of the United States government. Deep learning has been applied to many medical image analysis tasks for CAD [3234]. The results are summarized in Figure 2 to Figure 5. 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. The success of deep learning in many pattern recognition applications have brought excitement and high expectations that deep learning, or artificial intelligence (AI), can bring revolutionary changes in health care. This aspect is a significant problem given the fact that data Interpretation is one of the most crucial factors in such fields as medical image analysis. Baumgartner CF, Kamnitsas K, Matthew J, et al. The transferability of features decreases as the differences between the source domain and the target domain increase. 2012:1097-105. User training is crucial to ensure their understanding of the limitations and capability of CAD and thus avoid improper use or disillusion. First, the high detection rate of lung nodules depends on a large number of data sets, and at the same time depends on the accuracy of the annotation data. HHS Vulnerability Disclosure, Help These algorithms cover almost all aspects of our image processing, which mainly focus on classification, segmentation. At present, we mainly use the method of migration learning for small sample data to converge and achieve prediction results. 2017:2881-90. In recent years, deep learning has made breakthroughs in the fields of computer vision, speech recognition, natural language processing, audio recognition and bioinformatics (2). A data set of 260 DBT cases including 65 cancer, 65 benign, and 130 normal cases were read by 24 radiologists. However, there are certain limitations in migration learning. In another study [65], researchers developed a DCNN to identify normal mammograms from screening cases. The experiences of the single readers in the CAD arm were matched to those of the first readers in the double reading arm. (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. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S . Sahiner B, Chan H-P, Petrick N, Wagner RF, Hadjiiski LM. FastVentricle: cardiac segmentation with ENet. Although no CAD systems with new AI techniques have been subjected to large scale clinical trials to date, experiences from CAD use in screening mammography may provide some insights into what may be expected of CAD tools in the clinic [37]. The extracted features are then used as input predictor variables to a classifier, and a predictive model is formed by adjusting the weights of the various features based on the statistical properties of a set of training samples to estimate the probability that an image belongs to one of the states. 13.828 Impact Factor. reached 0.556 in three classifications of gastric cancer pathology (normal tissues, adenomas, cancer cells) (46). The ImageNet-pretrained AlexNet with 5 convolutional layers and 3 fully connected layers was appended with 2 additional fully connected layers (total of 5 fully connected layers) to reduce the classes from over 1000 to 2 (malignant and benign) and transfer-trained for the target task. Medical Imaging with Deep Learning 2021. None of the changes were statistically significant. Song HA, Lee SY. The convolutional layer of the encoder corresponds to the first 13 convolutional layers of VGG16. Finally, it discusses the possible problems and predicts the development prospects of deep learning medical imaging analysis. Its application mainly includes early tumor screening and benign and malignant diagnosis of tumor. Deng L, Yu D. Deep learning: methods and applications. Imagenet classification with deep convolutional neural networks. AlexNet model and adaptive contrast enhancement based ultrasound imaging classification. Since . 8600 Rockville Pike In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). Samala et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. (reprint with permission [49]). 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. proposed the network in network (NIN) structure, which uses global average pooling to reduce the risk of overfitting (6). The increase in recall rate for double reading without arbitration was more than twice of that for single reading with CAD. The experimental results show that the transfer learning based on the pre-trained CNN model is introduced to solve the problems in medical image analysis, and some effective results are produced. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network, A Fast Learning Algorithm for Deep Belief Nets. et al. Deep learning (DL) algorithms, a subset of ML, are AI-driven algorithms that can profoundly impact biomedical research, personalized medicine, and precision medicine . Such as U-Net (67.73%), SegNet (63.89%) and PSPNet (60.51%). By combining multiple nonlinear processing layers, the original data is abstracted layer by layer, and different levels of abstract features are obtained from the data and used for target detection, classification or segmentation. The decrease in sensitivity was a clear indication that the radiologists did not use CAD as a second opinion, which require the users to maintain their vigilance in interpretation, but over-relied on the CAD marks for recall decisions. The training set only included breast-level labeling without lesion annotation. Most of the studies reported very promising results, further boosting the hype of deep-learning-based CAD. 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. Although these clinical experiences of CAD were observed from screening mammography, they reveal the many challenges of implementing CAD or AI tools in the clinic and may provide some guidance on the development of the new generations of CAD for various applications in general. Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. At present, a number of excellent algorithms have emerged in the fields of driverlessness, natural language processing, computer vision, etc. . (reprint with permission [49]), ROI-based AUC on the DBT test set while varying the simulated DBT sample size available for transfer training. Thus, deep learning is generating a major impact in computer vision and medical imaging. We compared a variety of existing lung nodule detection methods and found that the deep learning algorithm greatly improved the detection rate of nodules compared with the traditional machine learning algorithm. et al. Figure 1 is an overview of some typical network structures in these areas. Because the DBT data set was small and mammogram data were relatively abundant, the pretrained AlexNet was transfer-trained in the first stage for the classification of masses on mammograms, which brought the AlexNet from an unrelated classification task on non-medical ImageNet data to a task (mammography) much closer to the target task (DBT). However, if all layers were allowed to be re-trained (C0), the transfer trained AlexNet did not perform well, probably because the mammography data was not large enough to fine-tune the large number of weights. applied a similar shift-invariant neural network for the detection of clusters of microcalcifications in 1994 [19]. sharing sensitive information, make sure youre on a federal 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. Foundations and Trends in Signal Processing 2014;7:197-387. In this chapter, we will discuss some issues and challenges in the development of deep-learning based CAD in medical imaging, as well as considerations needed for the future implementation of CAD in clinical use. Unlike Tensorflows static calculation graph, Pytorchs calculation graph is dynamic, and the calculation graph can be changed in real-time according to the calculation needs. The deep CNN used to update iterations with the development of deep learning techniques, from the earliest shallow CNN model to the deep CNN model or some combination models, and the use of migration learning, data augmentation and other new methods and techniques. Q: How do you see the use of Deep Learning continuing in medical imaging? Edited by Guotai Wang, Shaoting Zhang, Xiaolei Huang. Some of the challenges are discussed below. 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. A Survey on Deep Learning in Medical Image Analysis. This is an issue of great concern to medical and computer researchers, and intelligent imaging and deep learning provide a good answer. Isensee F, Jaeger PF, Full PM, et al. These visualization tools are the first steps to explore the inner workings of deep learning but they are still far from being able to translate the deep learning output to interpretable clinical decisions, especially for tasks more complex than lesion detection. The 3D-CAE pre-trained convolution filter is further applied to another set of data fields, such as the CAD Dementia pre-trained AD neuroimaging (ADNI) data set. Its very difficult to obtain large-scale medical annotation set, and transfer learning is an effective method to solve the problem of small data. Advances in neural information processing systems, Proc Advances in neural information processing systems (NIPS14). At the same time, its activation function is not sigmoid but adopted ReLU, and proved that the ReLU function is more effective. Accuracy and workflow efficiency are important considerations in clinical practice. Caffe features high-performance, seamless switching between CPU and GPU modes, and cross-platform support for Windows, Linux and Mac. The result shows that C1-frozen training provided the best test AUC for this task. For the shortcomings of CNN on the input size fixed requirements, He et al. 1). With the development of deep learning, more and more medical fields will apply deep learning technology, and future deep learning will not only focus on the single aspect of neuroimaging but also other aspects of genomics and bioinformatics.
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