论文标题
需要多少个X光片来重新培训深度学习系统以进行对象检测？
How many radiographs are needed to re-train a deep learning system for object detection?
论文作者
论文摘要
背景：X光片计算机视觉中的对象检测很大程度上受益于深度卷积神经网络中的进展，例如，可以在膝盖关节或椎间盘周围带有X射线照片注释X光片。深度学习是否能够在X光片中检测小（不到图像的1％）？在重新训练深度学习模型时，我们需要使用多少个X光片？ Methods: We annotated 396 radiographs of left and right carpi dorsal 75 medial to palmarolateral oblique (DMPLO) projection with the location of radius, proximal row of carpal bones, distal row of carpal bones, accessory carpal bone, first carpal bone (if present), and metacarpus (metacarpal II, III, and IV).将X光片和各自的注释分为集合，这些集合用于使用转移从Yolov5s学习创建的模型的交叉验证。 结果：使用96张X光片或更高的精度，召回和映射在0.95以上的模型，包括第一个腕骨，当训练为32个时代时，包括第一个腕骨。最佳模型需要双年时代才能学会检测与其他骨骼相比的第一个腕骨。 结论：可以对基于深度学习的原样对象检测模型的自由和开源状态进行重新训练，以使用100张X光片的X光片计算机视觉应用，并在0.95以上实现精度，回忆和映射。
Background: Object detection in radiograph computer vision has largely benefited from progress in deep convolutional neural networks and can, for example, annotate a radiograph with a box around a knee joint or intervertebral disc. Is deep learning capable of detect small (less than 1% of the image) in radiographs? And how many radiographs do we need use when re-training a deep learning model? Methods: We annotated 396 radiographs of left and right carpi dorsal 75 medial to palmarolateral oblique (DMPLO) projection with the location of radius, proximal row of carpal bones, distal row of carpal bones, accessory carpal bone, first carpal bone (if present), and metacarpus (metacarpal II, III, and IV). The radiographs and respective annotations were splited into sets that were used to leave-one-out cross-validation of models created using transfer learn from YOLOv5s. Results: Models trained using 96 radiographs or more achieved precision, recall and mAP above 0.95, including for the first carpal bone, when trained for 32 epochs. The best model needed the double of epochs to learn to detect the first carpal bone compared with the other bones. Conclusions: Free and open source state of the art object detection models based on deep learning can be re-trained for radiograph computer vision applications with 100 radiographs and achieved precision, recall and mAP above 0.95.
