Deep learning with convolutional neural network in radiology.
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important featu...
| Publicado en: | Japanese Journal of Radiology Vol. 36; no. 4; pp. 257 - 273 |
|---|---|
| Autores principales: | , , , , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Apr2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128864471&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128864471 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Apr2018 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128864471 128864471 NLM29498017 128864471 10.1007/s11604-018-0726-3 NLM29498017 128864471 ppf: 257 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning with convolutional neural network in radiology. aug: au: Yasaka, Koichiro Akai, Hiroyuki Kunimatsu, Akira Kiryu, Shigeru Abe, Osamu affil: Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, 108-8639, Tokyo, Japan sug: subj: Diagnostic Imaging Image Processing, Computer Assisted Methods Neural Networks (Computer) Information Science Methods Software Specialties, Medical Clinical Assessment Tools Scales ab: Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|