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A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
2008
Neural Information Processing Systems
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We present a "shape-aware" model which utilizes contour information for efficient and accurate labeling of features in the image. ...
We present an approach that combines bag-of-words and spatial models to perform semantic and syntactic analysis for recognition of an object based on its internal appearance and its context. ...
The authors would also like to thank Qihui Zhu for providing the code for extracting contours. ...
dblp:conf/nips/GuptaSD08
fatcat:yg7bv7es4fg5lcaqhgpa5riamy
A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
2018
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We present a "shape-aware" model which utilizes contour information for efficient and accurate labeling of features in the image. ...
We present an approach that combines bag-of-words and spatialmodels to perform semantic and syntactic analysis for recognition of an object based on its internal appearance and its context. ...
The authors would also like to thank Qihui Zhu for providing the code for extracting contours. ...
doi:10.1184/r1/6549938.v1
fatcat:rwf5lx5f4zajvefxcdptifuqqi
MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation
2021
NeuroImage
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Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for ...
Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated ...
consistency training and shape-aware learning methods for semi-supervised medical image segmentation. ...
doi:10.1016/j.neuroimage.2021.118568
pmid:34508895
fatcat:a3435uosm5ddjprfizn5obb6um
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.03296v1 fatcat:7mkvlp2gcjbhzcujtmgwv77dgu
We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from ...
Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have ...
[165] using shape awareness and local context constraints. ...
arXiv:2302.03296v2
fatcat:6bchgeat7beobpu72sfpgvfaua
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2207.14191v2 fatcat:j3o3vg5vd5dsxg5i3y6ovxlreyOther Versions
In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. ...
Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. ...
We also appreciate the efforts of literature collection and code implementations of SSL4MIS 6 and several public benchmarks. ...
arXiv:2207.14191v3
fatcat:gva2fzpos5efxfbod5kb4axm5a
Learning the Beauty in Songs: Neural Singing Voice Beautifier
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.13277v1 fatcat:yhafjjzngrc7xbopalvpsf5yli
In NSVB, we propose a novel time-warping approach for pitch correction: Shape-Aware Dynamic Time Warping (SADTW), which ameliorates the robustness of existing time-warping approaches, to synchronize the ...
Hence, we introduce Neural Singing Voice Beautifier (NSVB), the first generative model to solve the SVB task, which adopts a conditional variational autoencoder as the backbone and learns the latent representations ...
, which is based on a CVAE model allowing semi-supervised learning. ...
arXiv:2202.13277v2
fatcat:vh27zqyzyrfwdaz7sjsnjjoiue
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation
[article]
2022
arXiv
pre-print
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We do several sets of experiments for each module in the DASE-ProPillars detector that show that our model outperforms the SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test set ...
We apply domain adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset from the Regensburg Next project by applying transfer learning and achieve a 3D mAP@0.25 of 93.49% on the Car ...
ACKNOWLEDGMENT This work was funded by the Federal Ministry of Transport and Digital Infrastructure, Germany as part of the Providen-tia++ research project (Grant Number: 01MM19008A). ...
arXiv:2204.00132v1
fatcat:w7fxwi4vzng7dcjm6t4d63tn2m
Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training
[article]
2024
arXiv
pre-print
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2022 pre-print arXiv:2202.06104v1 fatcat:s7yuqfhtebh3jniua5tpkawway
In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation, which is a consistency-based method. ...
The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data. ...
Each branch implements two different tasks for image segmentation and geometrical shape awareness. ...
arXiv:2202.06104v2
fatcat:6zokapuzjzf3rkdwldciu5p2km
Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
2022
Applied Sciences
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In this paper, we propose the Attention V-Net architecture, which uses the 3D attention gate module, and applied it to the left atrium segmentation framework based on semi-supervised learning. ...
It can adaptively learn to highlight the salient features of images that are useful for image segmentation tasks. ...
We compare our framework with four semi-supervised segmentation methods, including entropy minimization approch (Entropy Mini) [30] , uncertainty-aware mean teacher model (UA-MT) [11] , shape-aware adversarial ...
doi:10.3390/app12083764
fatcat:yncahumtpzdw7jfxvoys7rhtye
A Brief Survey on Weakly Supervised Semantic Segmentation
2022
International Journal of Online and Biomedical Engineering (iJOE)
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A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. ...
This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. ...
Variation of cross-entropy loss by adding a shape Shape aware loss based coefficient used in cases of hard-to-segment boundaries. ...
doi:10.3991/ijoe.v18i10.31531
fatcat:6klflaiecrdgrizzlpgybimt6q
A Survey on Domain Generalization for Medical Image Analysis
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2402.05035v1 fatcat:qgd65fpcgnau5bc3narm7fxxbi
Medical Image Analysis (MedIA) has emerged as a crucial tool in computer-aided diagnosis systems, particularly with the advancement of deep learning (DL) in recent years. ...
First, we provide a formal definition of domain shift and domain generalization in medical field, and discuss several related settings. ...
Typically, [Liu et al., 2020a] proposed a shape-aware meta-learning (SAML) scheme for the prostate MRI segmentation, rooted in gradient-based meta-learning. ...
arXiv:2402.05035v2
fatcat:mcyzmzacm5fdrhn3opnmrmuanu
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans
[article]
2023
arXiv
pre-print
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To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve highly effective semi-supervised segmentation. ...
Semi-supervised learning has made significant progress in medical image segmentation. ...
RELATED WORK A. Semi-Supervised Learning for Segmentation Semi-supervised segmentation networks aim to leverage labeled and unlabeled data to improve segmentation accuracy. ...
arXiv:2303.05126v1
fatcat:mjyrnmg76zhwxal3ic7oxj2jri
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
[article]
2021
arXiv
pre-print
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To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and ...
Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. ...
In [40] , a shape-aware method, which incorporated prior knowledge of the target organ shape into a CNN backbone, was proposed and achieved encouraging performance on liver segmentation task. ...
arXiv:2010.14808v2
fatcat:hsfrknwdlffovdtqyuoi5cp24a
Modality specific U-Net variants for biomedical image segmentation: A survey
[article]
2022
arXiv
pre-print
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In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment ...
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical ...
Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary ...
arXiv:2107.04537v4
fatcat:m5oqea5q6vhbhkerjmejder3hu
FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion
[article]
2022
arXiv
pre-print
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Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. ...
Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments' entangled size ...
The authors would like to thank Nikolay Jetchev for the positive energy and scientific discussions contributing to the success of this work. ...
arXiv:2206.11768v1
fatcat:5icc5fnjy5a37cv5e7fjpjud54
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