论文标题
CDNET：对比度截面网络，用于细粒度图像分类眼b-scan超声
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
论文作者
论文摘要
B扫描超声模式中图像的精确和快速分类对于诊断眼部疾病至关重要。然而，在超声波中区分各种疾病仍然挑战经验丰富的眼科医生。因此，在这项工作中开发了一种新型的对比度脱离网络（CDNET），旨在应对超声图像中眼异常的细粒度形象分类（FGIC）挑战，包括眼内肿瘤（IOT），视网膜脱离（RD），后巩膜菌皮（RD），后巩膜（PSS）（PSS）（PSS）和Vitre hemormhage（vhage）。 CDNET的三个基本组成部分分别是弱监督的病变定位模块（WSLL），对比度多Zoom（CMZ）策略和超明显的对比度截图损失（HCD-LOSS）。这些组件促进了在输入和输出方面的细粒度识别的特征分解。提出的CDNET在我们的ZJU Ocular超声数据集（Zjuuld）上进行了验证，该数据集由5213个样品组成。此外，在两个公共和广泛使用的胸部X射线FGIC基准上验证了CDNET的概括能力。定量和定性结果证明了我们提出的CDNET的功效，该CDNET在FGIC任务中实现了最先进的性能。代码可在以下网址获得：https：//github.com/zeroonegame/cdnet-for-ous-fgic。
Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tackle the fine-grained image categorization (FGIC) challenges of ocular abnormalities in ultrasound images, including intraocular tumor (IOT), retinal detachment (RD), posterior scleral staphyloma (PSS), and vitreous hemorrhage (VH). Three essential components of CDNet are the weakly-supervised lesion localization module (WSLL), contrastive multi-zoom (CMZ) strategy, and hyperspherical contrastive disentangled loss (HCD-Loss), respectively. These components facilitate feature disentanglement for fine-grained recognition in both the input and output aspects. The proposed CDNet is validated on our ZJU Ocular Ultrasound Dataset (ZJUOUSD), consisting of 5213 samples. Furthermore, the generalization ability of CDNet is validated on two public and widely-used chest X-ray FGIC benchmarks. Quantitative and qualitative results demonstrate the efficacy of our proposed CDNet, which achieves state-of-the-art performance in the FGIC task. Code is available at: https://github.com/ZeroOneGame/CDNet-for-OUS-FGIC .
