Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1\% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space.
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Division AI4Health, Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学AI4Health部门）；University Institute for Diagnostic and Interventional Radiology, Klinikum Oldenburg AöR（奥尔登堡医院大学诊断与介入放射学研究所）
Classification of compact radio sources in the Galactic plane with supervised machine learning
INAF - Osservatorio Astrofisico di Catania（意大利国家天体物理研究所卡塔尼亚天体观测台）；Department of Physics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校物理系）；Western Sydney University（西悉尼大学）；CSIRO Space & Astronomy（澳大利亚联邦科学与工业研究组织空间与天文分部）；Thüringer Landessternwarte Tautenburg (TLS)（图林根州陶滕堡天文台）
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
Vingroup Big Data Institute（Vingroup大数据研究所）；Hanoi University of Science and Technology（河内理工大学）；VinUniversity（Vin大学）；Yale University（耶鲁大学）
在临床实践中，X线片被用作识别脊柱异常的最重要的成像工具。然而，脊柱骨病变的评价对放射科医生来说是一项具有挑战性的任务。这项工作旨在开发和评估一个基于深度学习的框架，名为VinDr-SpineXR，用于脊柱X射线异常的分类和定位。首先，我们建立了一个大型数据集，包括来自5,000项研究的10,468张脊柱X射线图像，每个图像都由经验丰富的放射科医生手动注释，并在13个类别的异常发现周围使用边界框。使用这个数据集，我们然后训练一个深度学习分类器来确定脊柱扫描是否异常，并训练一个检测器来定位13个发现中的7个关键发现。VinDr-SpineXR在来自1，000项研究的2，078张图像的测试集上进行评估，该测试集与训练集分开。它证明了图像级分类任务的受试者工作特征曲线下面积（AUROC）为88.61%（95% CI 87.19%，90.02%），病灶级定位任务的平均平均精度（mAP@0.5）为33.56%。这些结果可作为概念验证，并为该方向的未来研究设定了基线。为了鼓励进步，数据集，代码和训练的深度学习模型都是公开的。
