Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care system.Timely diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the disease.This study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with segmentation.First,the data were preprocessed.An optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance problem.Second,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning network.Third,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease severity.In this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

Active self-training for weakly supervised 3D scene semantic segmentation

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

Scribble-Supervised Video Object Segmentation

Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

Lesion region segmentation via weakly supervised learning

Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance.However,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation difficult.Methods:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation.First,a two-branch network is designed for joint disease classification and seed region generation.The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network.Different from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field diseases.We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed regions.Results:Experimental results show that our system achieves better lesion region segmentation results than state of the arts.In addition to crop images,our method is also applicable to general scattered object segmentation.We demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)method.Conclusion:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.

Continuous gradient fusion class activation mapping: segmentation of laser-induced damage on large-aperture optics in dark-field images

Segmenting dark-field images of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance but rely on a large number of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially underactivated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method, continuous gradient class activation mapping(CAM) and its nonlinear multiscale fusion(continuous gradient fusion CAM). The method redesigns backpropagating gradients and nonlinearly activates multiscale fused heatmaps to generate more fine-grained class activation maps with an appropriate activation degree for different damage site sizes. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms.

基于改进boxlevelset的叶片显微图像气孔分割方法

深度学习技术已被用于气孔分割任务。然而,训练数据的标注是机械且耗时的人工过程,特别是在数据集比较大的时候。为了减少标注时的工作量,研究提出一种基于弱监督模型Boxlevelset的气孔分割方法。将原模型的特征提取网络ResNet50替换为ResNest50,并且在特征提取过程中引入CBAM模块。以黑杨气孔为研究对象,该方法可有效分割出气孔,F1得分为79.89。所提出的方法减少了标注训练数据所需的时间,同时保证了分割精度,从而显著减少了训练气孔分割网络所需的工作量。

遥感大数据赋能青藏高原陆表水体空间分布信息认知

遥感大数据对赋能水利新质生产力发展具有显著作用,尤其在提升水资源调查、监测与管理效率方面展现出强大潜力。青藏高原被称为“亚洲水塔”,湖泊河流众多,对气候变化也最为敏感,然而由于地理位置特殊、地形环境复杂等因素,青藏高原水资源分布的精细化认知和监测一直是水利工作面临的一项挑战。针对青藏高原地区缺乏高空间分辨率陆表水体信息问题,提出分级弱监督信息挖掘策略,生成了青藏高原地区2 m分辨率陆表水体空间分布信息产品。经评估,产品总体精度为91.36%,用户精度为85.39%,生产者精度为91.71%,显示出较高的准确性和可靠性。研究成果不仅有助于深化对青藏高原地区水资源分布状况的认识,也展示出遥感大数据在复杂地形区域的应用潜力和价值,为水资源管理、生态保护以及区域可持续发展提供有力的数据支撑和决策依据。

基于缩影的多时相遥感语义变化检测方法

针对高分辨率遥感图像标签稀缺和标签技术增长缓慢限制了多时相语义变化检测发展的问题,提出了采用有噪声、低分辨率的弱标签替代高分辨率标签进行多时相语义变化检测的方法。首先,采用低分辨率卫星数据平滑高分辨率遥感图像输入的质量差异。其次,通过将缩影(epitomes)模型和标签超分辨率算法作为统计推理算法相结合的方法预估高分辨率遥感图像分类图,并拟合一个小型FCN网络对生成的遥感图像分类图进行后处理来改善其分类的效果。最后,通过对比不同时相土地覆盖分类图像之间的差异得出变化检测结果。结果表明,本研究提出的方法与其他多时相语义变化检测方法 FCN/all相比,平均交并比(mIoU)提高了8.9个百分点,能够有效检测土地覆盖分类变化。

前背景信息一致的边界框弱监督息肉分割网络

准确的息肉分割对结直肠癌的诊断和治疗具有重要意义。由于标注准确的像素级掩码成本很高,现在的息肉分割方法严重受到像素标注短缺的影响,而粗略的边界框标注更易获得。因此提出一个通用性高、即插即用的弱监督组件PolypBox,其可以将现有全监督的息肉分割方法转换成仅使用边界框标注的息肉分割方法。该模块由掩码投影损失、像素表示模块、前背景搜索损失和邻域像素一致性损失组成。首先设计像素表示模块从特征图中学习每个像素的特征表示(embedding),根据边界框的位置信息,使用K-Means分别聚类属于前背景的多个原型;然后提出前背景搜索损失将边框内的像素点与前背景的原型进行搜索匹配建立约束;在边界框内部设计掩码投影损失约束模型预测息肉的位置,最后提出邻域像素一致性损失,令具有邻域相似的像素点对的息肉预测结果保持一致。为验证算法的有效性,在CVC-300和Kvasir等4个具有挑战性的数据集和mean Dice等6个指标上与主流息肉分割网络进行对比,其mean Dice达到0.810,有着不输于目前主流全监督息肉分割方法的分割性能,同时验证了该方法的通用性。

基于类激活图的弱监督皮肤镜图像分割方法

皮肤镜图像中病灶区域的精确分割是实现皮肤病自动化检测的关键步骤。现存的皮肤镜图像分割方法主要基于全监督图像分割,这需要大量的像素标注,费时费力。针对此问题,提出一种基于类激活图(CAM)的弱监督皮肤镜图像分割方法。首先,对原始图像进行预处理,去除图像中的毛发并对图像进行颜色归一化处理;然后,结合图像的多尺度输入,并在显著图的引导下,通过特征提取网络得到图像的类激活图;之后,将得到的类激活图通过条件随机场得到伪掩膜;最后,使用伪掩膜训练分割网络。在ISIC2017数据集上评估所提方法,结果显示,所提方法生成的伪掩膜的Dice系数达到82.64%,相似性系数达到71.92%,灵敏度达到90.01%,表明所提方法能够在大量减少人工标注工作量的同时生成高质量的伪掩膜。

基于多任务学习的弱监督皮肤图像分割

为了降低网络受到数据集标注的限制,提出了一种基于多任务学习的弱监督医学图像语义分割模型,仅使用图像级真实标签实现了优异的医学图像分割性能.该模型利用图像分类任务和显著性检测任务辅助语义分割任务使之达到更好的分割效果,图像分类分支使用重建类激活图的方法降低类激活图过激活和欠激活的概率,同时设计了跨任务语义挖掘模块学习显著性检测任务和语义分割任务间的相似性用于优化特征图.融合类激活图和显著图生成像素级伪标签,并在训练过程中迭代优化伪标签,提升网络的分割性能.实验证明提出的方法在ISBI2016和ISIC2017皮肤图像数据集的平均交并比指标分别为68.24%和60.92%,远高于其他先进的弱监督语义分割算法.

弱监督语义分割的对抗学习方法

大多数弱监督语义分割的解决方案都利用图像级监督信息产生的类激活特征图进行训练学习。类激活特征图只能发现目标最具判别力的部分,它与真实的像素级标签信息存在较大差距,所以训练效果并不理想。对来自原图像及其仿射变化图像的类激活特征图进行对抗学习来达到更好的训练效果。首先将图像及对其进行仿射变化得到的图像输入孪生网络,使用图像级分类标签得到各自的类激活特征图,然后将这两组类激活特征图输入辨别网络进行对抗学习,训练孪生网络使得原图像与其仿射变化图像的类激活特征图逼近,从而有效利用等变注意力机制,学习更多的有效信息并缩小类激活特征图和真实的像素级标签之间的差距,提高弱监督的性能。在PASACAL VOC 2012数据集上,在验证集上的平均交并比为63.7%,测试集上的平均交并比为65.7%,与当前其他先进弱监督语义分割的方法进行对比,验证集与测试集上的平均交并比提高了1.2%和1.3%。该对抗性学习方案能有效利用等变注意力机制,学习更多的有效信息并缩小类激活特征图和真实的像素级标签之间的差距,提高弱监督的性能且达到了良好的分割效果。

基于弱监督学习的玉米苗期植株图像实例分割方法

基于有监督深度学习的图像分割任务通常利用像素级标签来保证模型的训练和测试精度,但受植株复杂形态影响,保证像素级标签精度的同时,时间成本也显著提高。为降低深度模型训练成本,同时保证模型能够有较高的图像分割精度,该研究提出一种基于边界框掩膜的深度卷积神经网络(Bounding-box Mask Deep Convolutional Neural Network,BM-DCNN),在有监督深度学习模型中融入伪标签生成模块,利用伪标签代替真值标签进行网络训练。试验结果表明,伪标签与真值标签的平均交并比为81.83%,平均余弦相似度为86.14%,高于Grabcut类方法生成伪标签精度(与真值标签的平均交并比为40.49%,平均余弦相似度为61.84%);对于玉米苗期图像(顶视图)计算了三种人工标注方式的时间成本,边界框标签为2.5 min/张,涂鸦标签为15.8 min/张,像素级标签为32.4 min/张;利用伪标签样本进行训练后,BM-DCNN模型的两种主干网络当IoU值大于0.7时(AP70),BM-DCNN模型对应的实例分割精度已经高于有监督模型。BM-DCNN模型的两种主干网络对应的平均准确率分别为67.57%和75.37%,接近相同条件下的有监督实例分割结果(分别为67.95%和78.52%),最高可达到有监督分割结果的99.44%。试验证明BM-DCNN模型可以使用低成本的弱标签实现高精度的玉米苗期植株图像实例分割,为基于图像的玉米出苗率统计以及苗期冠层覆盖度计算提供低成本解决方案及技术支持。

基于弱监督宫颈细胞图像的语义分割方法

基于弱监督标签的神经网络算法是医学领域的研究热点之一。针对医学领域缺少标签数据以及细胞质、细胞核分割不精确的问题,文中提出一种基于弱监督宫颈细胞图像的语义分割算法。该算法首先用无监督K-means作为标注函数生成细胞图像分割标签。然后,通过改进的Encoder-Decoder网络进行训练。文中引入CRF作为网络的最后一层以整合图片全局信息,优化分割结果。将标签及预测图像分3次进行优化训练以达到对细胞图像的像素级分类。在宫颈细胞图片数据集上对文中所提算法进行验证,实验结果表明,该算法具有良好的泛化能力,准确率高达96.7%。

基于一维卷积神经网络的实时心脏按压评估

在评估胸外心脏按压加速度波形时,现有的利用加速度波形积分计算胸外心脏按压距离的方法多数存在积分漂移、误差累积的问题。在波形分割和标签修正的基础上,提出一种基于一维卷积神经网络的胸外心脏按压波形的识别算法。对滤波后的数据进行脉冲识别,使用滑动窗口模型分割识别后的脉冲得到单次按压的加速度波形,根据数据离散程度对标签进行修正,解决标签可信度低的问题,在此基础上运用学习率衰减、Adam算法等构建一维卷积神经网络模型并进行优化。实验结果表明,该算法基于一维卷积神经网络的分类正确率达到99.4%,对比传统的积分算法、BP神经网络算法提升近5%,且不受按压遮挡、电磁波干扰等因素的影响,对于胸外心脏按压评估具有良好的效果。

弱监督学习语义分割方法综述

近年来,深度神经网络的运用使得语义分割技术得到了迅速的发展,然而这些方法在取得更好的分割结果的同时,对训练样本的需求也更多。一般的监督学习语义分割方法要求对训练集中每张图片进行逐像素的标注,这种昂贵的标注需要消耗大量的人力和时间,限制了语义分割算法在工程实际中的应用。弱监督学习语义分割能够使用对象边界框、图片类别标签等标注进行训练,从而大幅度地降低标注成本。文章对弱监督学习语义分割方法进行了分类和对比,并使用电网场景中的图片对方法进行了应用探索,说明了该类方法在工程中实际使用的可行性。

带尺寸约束的弱监督眼底图像视盘分割

目的医学图像的像素级标注工作需要耗费大量的人力。针对这一问题,本文以医学图像中典型的眼底图像视盘分割为例,提出了一种带尺寸约束的弱监督眼底图像视盘分割算法。方法对传统卷积神经网络框架进行改进,根据视盘的结构特点设计新的卷积融合层,能够更好地提升分割性能。为了进一步提高视盘分割精度,本文对卷积神经网络的输出进行了尺寸约束,同时用一种新的损失函数对尺寸约束进行优化,所提的损失公式可以用标准随机梯度下降方法来优化。结果在RIM-ONE视盘数据集上展开实验,并与经典的全监督视盘分割方法进行比较。实验结果表明,本文算法在只使用图像级标签的情况下,平均准确识别率(mAcc)、平均精度(mPre)和平均交并比(mIoU)分别能达到0. 852、0. 831、0. 827。结论本文算法不需要专家进行像素级标注就能够实现视盘的准确分割,只使用图像级标注就能够得到像素级标注的分割精度。缓解了医学图像中像素级标注难度大的问题。