2.为实现大分辨率图像上的小缺陷检测任务,网络结构设计需要具有如下特点,1)大感受野,即大的卷积核2)获取小尺寸特征细节的能力。 segmentation network:额外的下采样层和在深层网络中采用大的卷积核来提升感受野尺寸。浅层是选的通道比较少,深层选的通道多,获取更多感受野较大的feature。模型8倍下采样,全卷积结构。 d...
Segmentation-Based Deep-Learning Approach for Surface-Defect(2019.06) 这篇论文主要是利用分割模型和分类模型进行表面瑕疵的检测,主要优势为:只需要25-30个有缺陷的样本就可完成分类,所用样本极少。 从目标检测、目标追踪最先进的文献来看,效果最好的模型都是建立在分割(segmentation)的基础上,像目标检测中的Mask R...
deep-learningapproachforsurface-defectdetection检测对象:表面缺陷检测、裂纹检测(金属) 主要思想:本文主要采用了两个网络,一个是判别网络,一个是分割网络...了一种新的基于正样本训练的缺陷检测框架。基本检测的概念是建立一个重建网络,它可以修复样本中存在的缺陷区域,然后对输入样本与恢复样本进行比较,以指示缺陷区域...
Deep learningCNNMRIImage processingBrain tumor segmentationGliomaPurpose - Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor ...
A Deep Learning-Based Approach for Glomeruli Instance Segmentation from Multistained Renal Biopsy Pathologic Images Author links open overlay panelLei Jiang ∗, Wenkai Chen †, Bao Dong ‡, Ke Mei †, Chuang Zhu †, Jun Liu †, Meishun Cai ‡, Yu Yan ‡, Gongwei Wang §, ...
DeeplearningLeftventricleSegmentationabstractCardiacMRIhasbeenwidelyusedfornoninvasiveassessmentofcardiacanatomyandfunctionaswellasheartdiagnosis.TheestimationofphysiologicalheartparametersforheartdiagnosisessentiallyrequireaccuratesegmentationoftheLeftventricle(LV)fromcardiacMRI.Therefore,weproposeanoveldeeplearningapproachforthe...
—Deep learning techniques are proving instrumental in identifying, classifying, and quantifying patterns in medical images. Segmentation is one of the important applications in medical image analysis. The U-Net has become the predominant deep-learning approach to medical image segmentation tasks. Existing...
Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework Anand Shankar Manob Jyoti Saikia Shovan Barma npj Imaging (2024) External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington’s disease Robert Haase...
Single image segmentation and morphometric parameter evaluation took on average 1.33 s. To the best of our knowledge, this is the first time that a validated deep learning-based approach is applied in MG segmentation and evaluation for both upper and lower eyelids....
3.1. Performance of deep learning models for corn leaf segmentation The first stage of the novel disease severity estimation approach was to accurately segment the corn leaf using deep learning. The results of the three models on the 100 test images separated for evaluating the leaf segmentation ta...