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Search · four archives
14 papers · ranked by Valyu relevance
Farnoosh Arefi, Amir M. Mansourian, Shohreh Kasaei, Yawen Lu
Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
'Rebecca Raphael' 'Benjamin Raymond' 'Brian J. Leibowitz' 'Karen Schoedel' 'Rhonda M. Brand' 'Randall E. Brand' 'Jian Yu' 'Lin Zhang' 'Brenda Diergaarde' 'Robert E. Schoen' 'Aatur Singhi' 'Shikhar Uttam'] Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at…
Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari, Luoyao Chen + 4 more
'Mei Chen' 'Jinqian Pan' 'Craig Smuda' 'Jacopo Cirrone'] Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from…
Kunal Chaturvedi, Ali Braytee, Jun Li, Mukesh Prasad + 2 more
'Shengzong Zhou' 'Jingsha He'] This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net…
Ke Niu, Jiadong Guo, Ming Zhang, Zhongmin Guo + 4 more
Introduction Brain tissue segmentation in magnetic resonance imaging (MRI) is a fundamental step in quantitative neuroimaging and supports the analysis of structural brain alterations associated with aging. However, voxel-level annotation is expensive and labor-intensive, which limits the development of artificial…
Jinhee Park, Hyunmo Yang, Hyun-Jin Roh, Woonggyu Jung + 2 more
'Brian Gabrielli'] Simple Summary The cervix region segmentation significantly affects the accuracy of diagnosis when analyzing colposcopy. Detecting the cervix region requires manual, intensive, and time-consuming labor from a trained gynecologist. In this paper, we propose a deep learning-based automatic cervix…
Kun Fang, Kaiming Xu, Zhigang Wu, Tengchao Huang + 2 more
'Robert Sitnik'] This paper proposes a 3D point cloud segmentation algorithm based on a depth camera for large-scale model point cloud unsupervised class segmentation. The algorithm utilizes depth information obtained from a depth camera and a voxelization technique to reduce the size of the point cloud, and then uses…
Paulo Victor dos Santos, Marcella Scoczynski Ribeiro Martins, Solange Amorim Nogueira, Cristhiane Gonçalves + 3 more
'Solange Amorim Nogueira' 'Cristhiane Gonçalves' 'Rafael Maffei Loureiro' 'Wesley Pacheco Calixto' 'Ihssan S. Masad'] This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity…
Moiz Khan Sherwani, Aldo Marzullo, Elena De Momi, Francesco Calimeri
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack…
Jun Shu, Juncheng He, Ling Li
Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current…
Áron Fóthi, Adrián Szlatincsán, Ellák Somfai, Yun Zhang
A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture…
Yinyin Peng, Hui Feng, Tao Chen, Bo Hu + 4 more
'Jian Xiong' 'Sukun Tian'] Most existing point cloud instance segmentation methods require accurate and dense point-level annotations, which are extremely laborious to collect. While incomplete and inexact supervision has been exploited to reduce labeling efforts, inaccurate supervision remains under-explored. This…
Jianfei Liu, Christopher Parnell, Ronald M. Summers
Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. In our previous work, a dual-branch network was developed to segment single-class edematous adipose tissue. Its inputs include a few strong labels…
Hyun-Cheol Park, Sahadev Poudel, Raman Ghimire, Sang-Woong Lee
Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve…