These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
129 related articles for article (PubMed ID: 37116670)
21. An enhanced OCT image captioning system to assist ophthalmologists in detecting and classifying eye diseases. Vellakani S; Pushbam I J Xray Sci Technol; 2020; 28(5):975-988. PubMed ID: 32597828 [TBL] [Abstract][Full Text] [Related]
22. Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism. Sun Y; Zhang H; Yao X J Biomed Opt; 2020 Sep; 25(9):. PubMed ID: 32940026 [TBL] [Abstract][Full Text] [Related]
23. Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks. Micko A; Placzek F; Fonollà R; Winklehner M; Sentosa R; Krause A; Vila G; Höftberger R; Andreana M; Drexler W; Leitgeb RA; Unterhuber A; Wolfsberger S Front Endocrinol (Lausanne); 2021; 12():730100. PubMed ID: 34733239 [TBL] [Abstract][Full Text] [Related]
24. Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks. Rong Y; Xiang D; Zhu W; Yu K; Shi F; Fan Z; Chen X IEEE J Biomed Health Inform; 2019 Jan; 23(1):253-263. PubMed ID: 29994378 [TBL] [Abstract][Full Text] [Related]
25. Classification of pachychoroid on optical coherence tomography using deep learning. Kang NY; Ra H; Lee K; Lee JH; Lee WK; Baek J Graefes Arch Clin Exp Ophthalmol; 2021 Jul; 259(7):1803-1809. PubMed ID: 33616757 [TBL] [Abstract][Full Text] [Related]
26. Towards more efficient ophthalmic disease classification and lesion location via convolution transformer. Wen H; Zhao J; Xiang S; Lin L; Liu C; Wang T; An L; Liang L; Huang B Comput Methods Programs Biomed; 2022 Jun; 220():106832. PubMed ID: 35525213 [TBL] [Abstract][Full Text] [Related]
27. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images. Shi F; Cai N; Gu Y; Hu D; Ma Y; Chen Y; Chen X Phys Med Biol; 2019 Sep; 64(17):175010. PubMed ID: 31342925 [TBL] [Abstract][Full Text] [Related]
28. INTERNAL LIMITING MEMBRANE PEELING VERSUS NONPEELING TO PREVENT EPIRETINAL MEMBRANE DEVELOPMENT IN PRIMARY RHEGMATOGENOUS RETINAL DETACHMENT: A Swept-Source Optical Coherence Tomography Study With a New Postoperative Classification System. Arias L; Padrón-Pérez N; Flores-Moreno I; Giralt L; Cobos E; Lorenzo D; García-Bru P; Dias B; Caminal JM Retina; 2020 Jul; 40(7):1286-1298. PubMed ID: 31313717 [TBL] [Abstract][Full Text] [Related]
29. Convolutional neural network-based automatic detection of follicle cells in ovarian tissue using optical coherence tomography. Saito K; Motani Y; Takae S; Suzuki N; Tsukada K Biomed Phys Eng Express; 2020 Nov; 6(6):. PubMed ID: 34035193 [TBL] [Abstract][Full Text] [Related]
30. OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images. A P S; Kar S; S G; Gopi VP; Palanisamy P Comput Methods Programs Biomed; 2021 Mar; 200():105877. PubMed ID: 33339630 [TBL] [Abstract][Full Text] [Related]
31. Fully automated detection of retinal disorders by image-based deep learning. Li F; Chen H; Liu Z; Zhang X; Wu Z Graefes Arch Clin Exp Ophthalmol; 2019 Mar; 257(3):495-505. PubMed ID: 30610422 [TBL] [Abstract][Full Text] [Related]
32. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Ryu G; Lee K; Park D; Park SH; Sagong M Sci Rep; 2021 Nov; 11(1):23024. PubMed ID: 34837030 [TBL] [Abstract][Full Text] [Related]
33. A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography. Ryu G; Lee K; Park D; Kim I; Park SH; Sagong M Transl Vis Sci Technol; 2022 Feb; 11(2):39. PubMed ID: 35703566 [TBL] [Abstract][Full Text] [Related]
34. Self-supervised iterative refinement learning for macular OCT volumetric data classification. Qiu J; Sun Y Comput Biol Med; 2019 Aug; 111():103327. PubMed ID: 31302456 [TBL] [Abstract][Full Text] [Related]
35. Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks. Bareja R; Mojahed D; Hibshoosh H; Hendon C Appl Opt; 2022 May; 61(15):4458-4462. PubMed ID: 36256284 [TBL] [Abstract][Full Text] [Related]
36. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Perdomo O; Rios H; Rodríguez FJ; Otálora S; Meriaudeau F; Müller H; González FA Comput Methods Programs Biomed; 2019 Sep; 178():181-189. PubMed ID: 31416547 [TBL] [Abstract][Full Text] [Related]
37. Is choroidal vascularity index a useful marker in different stages of idiopathic epiretinal membranes? Gediz BS; Doguizi S; Ozen O; Sekeroglu MA Photodiagnosis Photodyn Ther; 2021 Mar; 33():102110. PubMed ID: 33242656 [TBL] [Abstract][Full Text] [Related]
38. Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network. Hazra D; Byun YC; Kim WJ Comput Methods Programs Biomed; 2022 Sep; 224():107019. PubMed ID: 35878483 [TBL] [Abstract][Full Text] [Related]
39. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion. Zabihollahy F; Schieda N; Krishna S; Ukwatta E Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661 [TBL] [Abstract][Full Text] [Related]
40. Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT. Sonobe T; Tabuchi H; Ohsugi H; Masumoto H; Ishitobi N; Morita S; Enno H; Nagasato D Int Ophthalmol; 2019 Aug; 39(8):1871-1877. PubMed ID: 30218173 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]