作者还训练了MedYOLO的大版本(MedYOLO-L)在腹部数据集上,早停发生在第968个Epoch,在BRaTS数据集上,它未能收敛。作者对两个框架在IoU 0.5处的平均精度(mAP)进行了报告。 对于MedYOLO,作者报告了COCO指标的3D版本,该指标计算了0.5到0.95之间IoU的平均mAP,间隔为0.05,正如YOLOv5所报告的。nnDetection框架只报告了IoU...
MedYOLO的训练过程紧密遵循YOLOv5的模式,但进行了适应3D医学图像的调整。 与YOLOv5相似的训练方法:MedYOLO在训练上几乎完全遵循YOLOv5的方法,使用了几乎相同的超参数(用于指导训练过程的参数),唯一的区别是增加了一些用于数据增强的超参数。 数据增强的调整:由于MedYOLO用于处理灰度图像(3D医学图像通常是灰度的),某些YO...
We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset,...
In this study, we propose an efficient end-to-end multi-task network capable of concurrently performing object detection and semantic segmentation called YOLO-Med. Our model employs a backbone and a neck for multi-scale feature extraction, complemented by the inclusion of two task-specific decoder...
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通过与现有的药物检测系统进行对比,我们发现“med_detect”数据集在提高YOLOv8模型的检测精度和召回率方面表现优异。这一成果不仅证明了数据集的有效性,也为未来的药物检测研究提供了宝贵的参考。 总之,“med_detect”数据集的构建和应用为药物检测领域带来了新的机遇。通过与YOLOv8模型的结合,我们期待能够在药物识别...
5-json格式转换成yolo-v3所需输入.mp4 06:15 6-完成输入数据准备工作.mp4 08:46 7-训练代码与参数配置更改.mp4 10:30 8-训练模型并测试效果.mp4 07:08 1-V4版本整体概述.mp4 06:04 2-V4版本贡献解读.mp4 04:36 3-数据增强策略分析.mp4 09:00 4-DropBlock与标签平滑方法.mp4 08:20 5-损失函数遇到...
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