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Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation [article]

Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, Alan Yuille
2019 arXiv   pre-print
To address the background ambiguity in these partially-labeled datasets, we propose Prior-aware Neural Network (PaNN) via explicitly incorporating anatomical priors on abdominal organ sizes, guiding the  ...  Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention.  ...  Prior-aware Neural Network Our work aims to address the multi-organ segmentation problem with the help of multiple existing partially-labeled datasets.  ... 
arXiv:1904.06346v2 fatcat:iknxol6yafbvjaqtgt72ixpfme

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Generation 449 A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation 450 Autofocus Layer for Semantic Segmentation 451 A Framework for Identifying Diabetic Retinopathy  ...  Microabcess in skin biopsy images 321 A Novel Bayesian Model Incorporating Deep Neural Network and Statistical Shape Model for Pancreas Segmentation 327 One-pass Multi-task Convolutional Neural Networks  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Context-aware virtual adversarial training for anatomically-plausible segmentation [article]

Ping Wang and Jizong Peng and Marco Pedersoli and Yuanfeng Zhou and Caiming Zhang and Christian Desrosiers
2021 arXiv   pre-print
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance  ...  To solve this problem, we present a Context-aware Virtual Adversarial Training (CaVAT) method for generating anatomically plausible segmentation.  ...  Prior-aware neural network for partially-supervised multi-organ segmentation. arXiv preprint arXiv:1904.06346 . 8 Zhou, Y., Wang, Y., Tang, P., Bai  ... 
arXiv:2107.05532v2 fatcat:ksdfzfugqjdqretsilmvrcxodu

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  They developed a prior-aware neural network that explicitly incorporated anatomical priors on abdominal organ sizes as domain-specific knowledge to guide the training process. Dmitriev et al.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  They developed a prior-aware neural network that explicitly incorporated anatomical priors on abdominal organ sizes as domain-specific knowledge to guide the training process. Dmitriev et al.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets [article]

Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen
2020 arXiv   pre-print
Thus, DoDNet is able to segment multiple organs and tumors, as done by multiple networks or a multi-head network, in a much efficient and flexible manner.  ...  The information of the current segmentation task is encoded as a task-aware prior to tell the model what the task is expected to solve.  ...  Intuitively, this task prior should be encoded into the model for task-awareness. Chen et al.  ... 
arXiv:2011.10217v1 fatcat:jjsyq4yqbrgfpaa6i6kqfvipom

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1972-1984 Attention-Aware Multi-Task Convolutional Neural Networks. Lyu, K., +, TIP 2020 1867-1878 Burst Ranking for Blind Multi-Image Deblurring.  ...  Wang, Y., +, TIP 2020 5386-5395 Convolutional neural nets A Joint Relationship Aware Neural Network for Single-Image 3D Human Pose Estimation.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 9332-9344 Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection.  ...  ., +, TIP 2021 8999-9013 Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation. Chen, L., +, TIP 2021 2313-2324 Spatially-Aware Context Neural Networks.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation [article]

Minyoung Chung, Jingyu Lee, Minkyung Lee, Jeongjin Lee, Yeong-Gil Shin
2019 arXiv   pre-print
To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted  ...  The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.  ...  The deep contour-aware network [28] has been developed to depict clear contours with a multi-task framework.  ... 
arXiv:1808.00739v5 fatcat:24r7mzcbsfdz7njb2jttsgkewm

Marginal loss and exclusion loss for partially supervised multi-organ segmentation [article]

Gonglei Shi, Li Xiao, Yang Chen, S. Kevin Zhou
2020 arXiv   pre-print
In this paper, we investigate how to learn a single multi-organ segmentation network from a union of such datasets.  ...  Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is,  ...  The Prior-aware Neural Network (PaNN) refers to the which adds a prioraware loss to learn partially labeled data. The pyramid input and pyramid output (PIPO) refers to the work by Fang et al.  ... 
arXiv:2007.03868v1 fatcat:rqywz63bjfbihgbadxpanqsppy

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TPAMI March 2021 887-901 Ordinal Multi-Task Part Segmentation With Recurrent Prior Generation.  ...  ., +, TPAMI July 2021 2245-2256 Location awareness Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good! [article]

Hoel Kervadec and Houda Bahig and Laurent Letourneau-Guillon and Jose Dolz and Ismail Ben Ayed
2021 arXiv   pre-print
Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentations.  ...  Inspired by recent works in constrained optimization for deep networks, we propose a way to use those descriptors to supervise segmentation, without any pixel-level label.  ...  Prior-aware neural network for partially-supervised multi-organ segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10672-10681, 2019.  ... 
arXiv:2105.00859v1 fatcat:ubd77k2nnrhrvmfdxbeqt4l7cm

Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness

Tran-Dac-Thinh Phan, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee
2021 Applied Sciences  
Variations in size and color, the fuzzy boundary and the low contrast between lesion and normal skin are the adverse factors for deficient or excessive delineation of lesions, or even inaccurate lesion  ...  Moreover, concerning the large variation in size, the Selective Kernel modules, which are placed in the skip connections, transfer the multi-receptive field features from the encoder to the decoder.  ...  In this paper, we propose a new deep neural network based on the U-net architecture [15] for the skin lesion segmentation.  ... 
doi:10.3390/app11104528 fatcat:cb45on3uxrazrjxdckxowejqo4

Table of Contents

2021 2021 IEEE International Conference on Image Processing (ICIP)  
OBJECT DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS Neelanjan Bhowmik, Yona Falinie A.  ...  LEARNING FOR IMAGE AND VIDEO ANALYSIS, SYNTHESIS, AND RETRIEVAL 3 MLR-APPL-IVASR-3.1: LIGHTWEIGHT MULTI-BRANCH NETWORK FOR PERSON ............................................1129 RE-IDENTIFICATION Fabian  ... 
doi:10.1109/icip42928.2021.9506758 fatcat:5g2bwdt2efafjd2mubhxyv4m4y

Boundary-Aware Transformers for Skin Lesion Segmentation [chapter]

Jiacheng Wang, Lan Wei, Liansheng Wang, Qichao Zhou, Lei Zhu, Jing Qin
2021 Lecture Notes in Computer Science  
We propose a novel boundary-aware transformer (BAT) to comprehensively address the challenges of automatic skin lesion segmentation.  ...  Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.  ...  For example, TransUNet [6] , a hybrid architecture of CNN and transformer, performs well on Synapse multi-organ segmentation.  ... 
doi:10.1007/978-3-030-87193-2_20 fatcat:ya2h3civnbfhzdfvdrnov2oavi
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