OCTCube
OCTCube is a 3D foundation model for optical coherence tomography (OCT) developed by the University of Washington, comprising 26,605 3D OCT volumes (1.62 million 2D OCT images). It is pre-trained using 3D masked autoencoders to capture rich 3D structures, enabling cross-dataset, cross-disease, cross-device, and cross-modality analysis for retinal disease diagnosis and prediction, demonstrating superior performance.
基本信息
About
OCTCube is a 3D foundation model for optical coherence tomography (OCT) developed by the University of Washington, comprising 26,605 3D OCT volumes (1.62 million 2D OCT images). It is pre-trained using 3D masked autoencoders to capture rich 3D structures, enabling cross-dataset, cross-disease, cross-device, and cross-modality analysis for retinal disease diagnosis and prediction, demonstrating superior performance.
http://arxiv.org/abs/2408.11227v1
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数据集说明
OCTCube 3D光学相干断层扫描基础模型 对应论文数据集（arXiv 预印本）。
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- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2408.11227v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:We present OCTCube-M, a 3D OCT-based multi-modal foundation model for jointly analyzing OCT and en face images. OCTCube-M first developed OCTCube, a 3D foundation model pre-trained on 26,685 3D OCT volumes encompassing 1.62 million 2D OCT images. It then exploits a novel multi-modal contrastive learning framework COEP to integrate other retinal imaging modalities, such as fundus autofluorescence and infrared retinal imaging, into OCTCube, efficiently extending it into multi-modal foundation models. OCTCube achieves best performance on predicting 8 retinal diseases, demonstrating strong generalizability on cross-cohort, cross-device and cross-modality prediction. OCTCube can also predict cross-organ nodule malignancy (CT) and low cardiac ejection fraction as well as systemic diseases, such as diabetes and hypertension, revealing its wide applicability beyond retinal diseases. We further develop OCTCube-IR using COEP with 26,685 OCT and IR image pairs. OCTCube-IR can accurately retrieve between OCT and IR images, allowing joint analysis between 3D and 2D retinal imaging modalities. Finally, we trained a tri-modal foundation model OCTCube-EF from 4 million 2D OCT images and 400K en face retinal images. OCTCube-EF attains the best performance on predicting the growth rate of geographic atrophy (GA) across datasets collected from 6 multi-center global trials conducted in 23 countries. This improvement is statistically equivalent to running a clinical trial with more than double the size of the original study. Our analysis based on another retrospective case study reveal
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