论文标题
部分可观测时空混沌系统的无模型预测
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans
论文作者
论文摘要
We present an algorithm for multi-scale tumor (chimeric cell) detection in high resolution slide scans.我们数据集中的肿瘤大小范围很广，对当前的卷积神经网络（CNN）构成了挑战，当图像特征很小（8像素）时，通常会失败。我们的方法修改了CNN中不同层的有效接受场，因此可以在单个正向传球中检测到具有广泛尺度的对象。 We define rules for computing adaptive prior anchor boxes which we show are solvable under the equal proportion interval principle.我们的CNN体系结构中的两种机制减轻了数据中普遍存在的非歧视特征的效果 - 一种中央凹检测算法，该算法结合了级联残留量 - 插入模块和反卷积模块以及其他上下文信息。 When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.
We present an algorithm for multi-scale tumor (chimeric cell) detection in high resolution slide scans. The broad range of tumor sizes in our dataset pose a challenge for current Convolutional Neural Networks (CNN) which often fail when image features are very small (8 pixels). Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward pass. We define rules for computing adaptive prior anchor boxes which we show are solvable under the equal proportion interval principle. Two mechanisms in our CNN architecture alleviate the effects of non-discriminative features prevalent in our data - a foveal detection algorithm that incorporates a cascade residual-inception module and a deconvolution module with additional context information. When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.
