Page 124 - 南京医科大学学报自然科学版
P. 124
第44卷第2期
·262 · 南 京 医 科 大 学 学 报 2024年2月
[10] 吴 洋,张红梅,尹立雪,等. 超声心动图心尖四腔心切 164:135-145
面图像质量智能评分研究[J]. 中华医学超声杂志(电 [17] TAHERI DEZAKI F,LIAO Z B,LUONG C,et al. Cardiac
子版),2023,20(1):97-102 phase detection in echocardiograms with densely gated re⁃
[11] 李 敬,刘宁宁,王笑一. 人工智能在超声诊断中的应 current neural networks and global extrema loss[J]. IEEE
用现状及展望[J]. 中国超声医学杂志,2022,38(5): Trans Med Imag,2018,38(8):1821-1832
595-598 [18] FIORITO A M,ØSTVIK A,SMISTAD E,et al. Detection
[12] FARHAD M,MASUD M M,BEG A. Deep learning based of cardiac events in echocardiography using 3D convolu⁃
cardiac phase detection using echocardiography imaging tional recurrent neural networks[C]//2018 IEEE Interna⁃
[C]//International Conference on Advanced Data Mining tional Ultrasonics Symposium(IUS).Kobe,Japan.IEEE,
and Applications.Cham:Springer,2022:3-17 2019:1-4
[13] DEZAKI F T,DHUNGEL N,ABDI A H,et al. Deep resid⁃ [19] LANE E S,AZARMEHR N,JEVSIKOV J,et al. Multibeat
ual recurrent neural networks for characterisation of cardi⁃ echocardiographic phase detection using deep neural net⁃
ac cycle phase from echocardiograms[C]//International works[J]. Comput Biol Med,2021,133:104373
Workshop on Deep Learning in Medical Image Analysis, [20] LIU Z,NING J,CAO Y,et al. Video swin transformer[C]//
International Workshop on Multimodal Learning for Clini⁃ 2022 IEEE/CVF Conference on Computer Vision and Pat⁃
cal Decision Support.Cham:Springer,2017:100-108 tern Recognition(CVPR). New Orleans,LA,USA.IEEE,
[14] KONG B,ZHAN Y Q,SHIN M,et al. Recognizing end⁃Di⁃ 2022:3192-3201
astole and end⁃Systole frames via deep temporal regres⁃ [21] OUYANG D,HE B,GHORBANI A,et al. Video⁃based AI
sion network [M]//OURSELIN S, JOSKOWICZ L, for beat⁃to⁃beat assessment of cardiac function[J]. Na⁃
SABUNCU M R,et al.,Eds.Lecture Notes in Computer ture,2020,580(7802):252-256
Science.Cham:Springer International Publishing,2016: [22] YIN H,ZHOU Y H,CAO L,et al. Channel prediction
264-272 with liquid time⁃constant networks:an online and adap⁃
[15] HE F X,LIU T L,TAO D C. Why ResNet works?Residu⁃ tive approach[C]//2021 IEEE 94th Vehicular Technology
als generalize[J]. IEEE Trans Neural Netw Learn Syst, Conference(VTC2021 ⁃ Fall). Norman,OK,USA.IEEE,
2020,31(12):5349-5362 2021:1-6
[16] QIN C,CHEN L M,CAI Z T,et al. Long short⁃term memo⁃ [收稿日期] 2023-08-08
ry with activation on gradient[J]. Neural Netw,2023, (本文编辑:戴王娟)