Akiba M - Search Results

1

Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.

An G, Akiba M, Omodaka K, Nakazawa T, Yokota H. An G, et al. Among authors: akiba m. Sci Rep. 2021 Mar 1;11(1):4250. doi: 10.1038/s41598-021-83503-7. Sci Rep. 2021. PMID: 33649375 Free PMC article.

2

Correlation of optic nerve microcirculation with papillomacular bundle structure in treatment naive normal tension glaucoma.

Kobayashi W, Kunikata H, Omodaka K, Togashi K, Ryu M, Akiba M, Takeuchi G, Yuasa T, Nakazawa T. Kobayashi W, et al. Among authors: akiba m. J Ophthalmol. 2014;2014:468908. doi: 10.1155/2014/468908. Epub 2014 Dec 9. J Ophthalmol. 2014. PMID: 25574382 Free PMC article.

3

Correlation of papillomacular nerve fiber bundle thickness with central visual function in open-angle glaucoma.

Kobayashi W, Kunikata H, Omodaka K, Togashi K, Ryu M, Akiba M, Takeuchi G, Yuasa T, Nakazawa T. Kobayashi W, et al. Among authors: akiba m. J Ophthalmol. 2015;2015:460918. doi: 10.1155/2015/460918. Epub 2015 Mar 2. J Ophthalmol. 2015. PMID: 25821587 Free PMC article.

4

3D evaluation of the lamina cribrosa with swept-source optical coherence tomography in normal tension glaucoma.

Omodaka K, Horii T, Takahashi S, Kikawa T, Matsumoto A, Shiga Y, Maruyama K, Yuasa T, Akiba M, Nakazawa T. Omodaka K, et al. Among authors: akiba m. PLoS One. 2015 Apr 15;10(4):e0122347. doi: 10.1371/journal.pone.0122347. eCollection 2015. PLoS One. 2015. PMID: 25875096 Free PMC article.

5

Clinical Factors Associated with Lamina Cribrosa Thickness in Patients with Glaucoma, as Measured with Swept Source Optical Coherence Tomography.

Omodaka K, Takahashi S, Matsumoto A, Maekawa S, Kikawa T, Himori N, Takahashi H, Maruyama K, Kunikata H, Akiba M, Nakazawa T. Omodaka K, et al. Among authors: akiba m. PLoS One. 2016 Apr 21;11(4):e0153707. doi: 10.1371/journal.pone.0153707. eCollection 2016. PLoS One. 2016. PMID: 27100404 Free PMC article.

6

OCT-Based Quantification and Classification of Optic Disc Structure in Glaucoma Patients.

Takada N, Omodaka K, Kikawa T, Takagi A, Matsumoto A, Yokoyama Y, Shiga Y, Maruyama K, Takahashi H, Akiba M, Nakazawa T. Takada N, et al. Among authors: akiba m. PLoS One. 2016 Aug 24;11(8):e0160226. doi: 10.1371/journal.pone.0160226. eCollection 2016. PLoS One. 2016. PMID: 27557112 Free PMC article.

7

Usefulness of axonal tract-dependent OCT macular sectors for evaluating structural change in normal-tension glaucoma.

Omodaka K, Kikawa T, Shiga Y, Tsuda S, Yokoyama Y, Sato H, Ohuchi J, Matsumoto A, Takahashi H, Akiba M, Nakazawa T. Omodaka K, et al. Among authors: akiba m. PLoS One. 2017 Oct 3;12(10):e0185649. doi: 10.1371/journal.pone.0185649. eCollection 2017. PLoS One. 2017. PMID: 28973002 Free PMC article.

8

Evaluation of retinal nerve fiber layer defect using wide-field en-face swept-source OCT images by applying the inner limiting membrane flattening.

Miura N, Omodaka K, Kimura K, Matsumoto A, Kikawa T, Takahashi S, Takada N, Takahashi H, Maruyama K, Akiba M, Yuasa T, Nakazawa T. Miura N, et al. Among authors: akiba m. PLoS One. 2017 Oct 27;12(10):e0185573. doi: 10.1371/journal.pone.0185573. eCollection 2017. PLoS One. 2017. PMID: 29077716 Free PMC article.

9

Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.

Omodaka K, An G, Tsuda S, Shiga Y, Takada N, Kikawa T, Takahashi H, Yokota H, Akiba M, Nakazawa T. Omodaka K, et al. Among authors: akiba m. PLoS One. 2017 Dec 19;12(12):e0190012. doi: 10.1371/journal.pone.0190012. eCollection 2017. PLoS One. 2017. PMID: 29261773 Free PMC article.

10

Comparison of Machine-Learning Classification Models for Glaucoma Management.

An G, Omodaka K, Tsuda S, Shiga Y, Takada N, Kikawa T, Nakazawa T, Yokota H, Akiba M. An G, et al. Among authors: akiba m. J Healthc Eng. 2018 Jun 19;2018:6874765. doi: 10.1155/2018/6874765. eCollection 2018. J Healthc Eng. 2018. PMID: 30018755 Free PMC article.

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