We begin by introducing imaging advancements over the decades, followed by general pre-analysis processes. Further, we describe the different kinds of deep learning models used for image analysis, after broadly classifying them into supervised and unsupervised learning. Here the chapter touches upon ...
This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, ...
Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essent...
C.; Helvie, M.A.; Neal, C.H. Explainable AI for medical imaging: Deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI. In Proceedings of the SPIE Medical Imaging 2020: Computer-Aided Diagnosis; International Society for Optics and Photonics: Bellingham, WA,...
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of...
4. Applications in 3D Medical Imaging 依然主要是计算机视觉方面的主要4个任务。 4.1 sgementation 在医疗图像领域使用3D DNN来进行病变区域分割的工作主要总结如下。 挑战:病变区域一般相对较小,增加了图片分割的困难。在不同的扫描过程中,病变区域的大小会出现差异,会导致训练样本的不均衡。 相关工作: Deep Medic...
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation. Recently, deep learning-based approaches have presented the state-of-the...
Machine learning Deep learning Medical imaging MRI 1. Introduction Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture. These are driven by breakthroughs in artificial neural networks, often termed deep learning, a set...
Medical imaging is a rich source of invaluable information necessary for clinical judgements. However, the analysis of those exams is not a trivial assignment. In recent times, the use of deep learning (DL) techniques, supervised or unsupervised, has been empowered and it is one of the current...
Chen Z, Pawar K, Ekanayake M et al (2022) Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges. J Digit Imaging, pp 1–27.https://doi.org/10.1007/s10278-022-00721-9 Zheng Y, Zhen B, Chen A (2020) A hybrid convolutional ne...