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Deep Learning

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. https://www.ncbi.nlm.nih.gov/pubmed/28070212 Comput Math Methods Med. 2016;2016:6215085. doi: 10.1155/2016/6215085. Epub 2016 Dec 14.Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.Li W1, Cao P1, Zhao D1, Wang J2.Author information1Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 110819, China; Co.. 더보기
Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks. https://www.ncbi.nlm.nih.gov/pubmed/28250804 Comput Math Methods Med. 2017;2017:3602928. doi: 10.1155/2017/3602928. Epub 2017 Jan 30.Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.Chande RD1, Hargraves RH2, Ortiz-Robinson N3, Wayne JS1.Author informationAbstractComputational models are useful tools to study the biomechanics of human joints. Their predi.. 더보기
Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611923/ Biomed Opt Express. 2017 Aug 10;8(9):4061-4076. doi: 10.1364/BOE.8.004061. eCollection 2017 Sep 1.Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.Xu Y1, Yan K2, Kim J2, Wang X2, Li C2, Su L1, Yu S1, Xu X1, Feng DD2.Author informationAbstractWorldwide, polypoidal choroidal .. 더보기
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. https://www.ncbi.nlm.nih.gov/pubmed/28973096 JAMA Ophthalmol. 2017 Sep 28. doi: 10.1001/jamaophthalmol.2017.3782. [Epub ahead of print]Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.Burlina PM1, Joshi N1, Pekala M1, Pacheco KD2, Freund DE1, Bressler NM3,4.Author information1The Johns Hopkins University Applied Physics Labor.. 더보기
Spoof Detection for Finger-Vein Recognition System Using NIR Camera http://www.mdpi.com/1424-8220/17/10/2261 Sensors (Basel). 2017 Oct 1;17(10). pii: E2261. doi: 10.3390/s17102261.Spoof Detection for Finger-Vein Recognition System Using NIR Camera.Nguyen DT1, Yoon HS2, Pham TD3, Park KR4.Author information1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. nguyentiendat@dongguk.edu.2Divisio.. 더보기
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. https://www.ncbi.nlm.nih.gov/pubmed/28974302 Comput Biol Med. 2017 Sep 27. pii: S0010-4825(17)30315-3. doi: 10.1016/j.compbiomed.2017.09.017. [Epub ahead of print]Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.Acharya UR1, Oh SL2, Hagiwara Y2, Tan JH2, Adeli H3.Author information1Department of Electronics and Computer Engineering, Ngee An.. 더보기
딥러닝을 통한 골연령 측정 - 하버드대 J Digit Imaging. 2017 Mar 8. doi: 10.1007/s10278-017-9955-8. [Epub ahead of print] Fully Automated Deep Learning System for Bone Age Assessment. Lee H1, Tajmir S1, Lee J1, Zissen M1, Yeshiwas BA1, Alkasab TK1, Choy G1, Do S2. Author information: 1 Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA. 2 Massachusetts Gener.. 더보기
중환자실의 모니터링 장비의 정보를 ANN으로 디자인해서, 48시간이내의 악화를 탐지 full text PDF(-) 중요도(임상적파급력, 유용성) :2.5 회기적신기술사용수준 : 2 날짜 : 2016-08 저널 : IEEE 공학논문 국가 : 미국 대학 또는 연구소 : 필라델피아 어린이병원, 소아과, 마취과 저자: 한줄요약 : 중환자실에 처음들어오면 첫48시간동안 여러 모니터링 장비들을 통해 수집한 각 기관의 정보를 통해, 데이터를 카테고라이즈하고, 피쳐들을 랭킹한뒤, ANN 에 넣어서, 임상적으로 악화되는 지를 탐지한다. (CDSS classifier, SOFA score, SAPS-III score). Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2520-2524. doi: 10.1109/EMBC.2016.7591243. Advanced analy.. 더보기
응급실에서 뇌졸중 조기 발견에 ANN with backprop 적용 full text PDF(-) 중요도(임상적파급력, 유용성) :3 회기적신기술사용수준 : 3 날짜 : 2017-06 저널 : Stroke / 신경과 의학저널 국가 : 미국 대학 또는 연구소 : 미국 테네시, 버지니아 공대, 의공학, 신경과 저자: 한줄요약 : 응급실에 뇌졸중 증상으로 온 환자들을 4시간 이내에, 진짜 뇌줄중인지 아니면 다른 질환인지를 구분하는데 ANN with backprop을 써서 구분하였다. 민감도 80%, 특이도 86%. . Stroke. 2017 Jun;48(6):1678-1681. doi: 10.1161/STROKEAHA.117.017033. Epub 2017 Apr 24. Novel Screening Tool for Stroke Using Artificial Neural Net.. 더보기

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