NLM IRP Seminar Schedule
UPCOMING SEMINARS
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May 14, 2024 Stanley Liang
Knowledge-driven Latent Diffusion For COVID-19 Pneumonia Radiology Pattern Synthesis -
May 21, 2024 Ziynet Kesimoglu
TBD -
May 23, 2024 Leslie Ronish
TBD -
May 28, 2024 Harutyun Saakyan
TBD -
May 30, 2024 Deepak Gupta
TBD
RECENT SEMINARS
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May 9, 2024 Pascal Mutz
The Riboviria protein structurome expands virus protein annotation and highlights protein relations -
May 2, 2024 OPEN
TBD -
April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome
Scheduled Seminars on April 11, 2023
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly. Late-stage AMD can develop into atrophic or neovascular AMD where atrophic AMD is the most common form. Geographic atrophy (GA) is the primary lesion in late atrophic AMD and is usually accompanied by very poor central vision. GA is predicted to affect more than five million people worldwide. Fast and accurate identification of eyes with GA could lead to improved management of the disease. GA can be detected on 2D imaging modalities, e.g., fundus autofluorescence (FAF) images and color fundus photographs (CFP). However, these modalities provide no details about the underlying layer structures and how they change. In this work, we use optical coherence tomography (OCT) volumetric scans for the detection task. OCT scans have the advantages of being easily available and providing volumetric context. For this purpose, we developed 3D convolutional neural networks (CNNs) as well as 2D CNNs. A dataset of 1,284 SD OCT scans from 311 participants was used to train networks, where cross-validation was used for evaluation with each testing set, containing no participant from the corresponding training set. En-face heatmaps and important regions at the B-scan level were used to visualize the outputs of networks, and three ophthalmologists graded the presence or absence of GA in them to assess the explainability of its detections. Compared to other networks, 3D CNNs achieved the best metrics, with the best accuracy of 0.93, AUC of 0.94, and APR of 0.91, and received the best gradings, of 0.98 and 0.68 on the en face heatmap and B-scan grading tasks, respectively.