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A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data

Hengji Cui, Dong Wei, Kai Ma, Shi Gu, Yefeng Zheng
2020 IEEE Transactions on Medical Imaging  
In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML).  ...  Medical image segmentation has achieved remarkable advancements using deep neural networks (DNNs).  ...  In summary, our contributions are three folds: • First, we propose the MRE-Net, a unified framework for generalized low-shot (one-and few-shot) medical image segmentation in case of scarcity of both annotations  ... 
doi:10.1109/tmi.2020.3045775 pmid:33338014 fatcat:vjyusba7fver7i6sk3qfz7k5du

Table of contents

2021 IEEE Transactions on Medical Imaging  
Summers 2642 A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data ............... ........................................................................... H.  ...  Mateus Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation ............................................................................  ... 
doi:10.1109/tmi.2021.3112022 fatcat:kcyeb3vh3zdafc746oq44zcjcu

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

Jialin Peng, Ye Wang
2021 arXiv   pre-print
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  application of deep learning models in medical image segmentation.  ...  [218] introduced a superpixel-based self-supervision technique for few-shot segmentation of medical images and showed the promising ability of generalization to unseen semantic classes. K.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

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

Jialin Peng, Ye Wang
2021 IEEE Access  
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  application of deep learning models in medical image segmentation.  ...  [218] introduced a superpixel-based selfsupervision technique for few-shot segmentation of medical images and showed the promising ability of generalization to unseen semantic classes. K.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [article]

Siddhesh Khandelwal, Raghav Goyal, Leonid Sigal
2021 arXiv   pre-print
Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples  ...  Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect.  ...  annotations, ranging from no data (zero-shot) to a few (few-shot); (2) We propose a general, unified, interpretable and flexible end-to-end framework that can adopt classifiers/detectors/segmentors for  ... 
arXiv:2006.07502v3 fatcat:5wtstqlbcfbdplg2qzbpmmyq4a

A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images

Peter Ardhianto, Jen-Yung Tsai, Chih-Yang Lin, Ben-Yi Liau, Yih-Kuen Jan, Veit-Babak-Hamun Akbari, Chi-Wen Lung
2021 Applied Sciences  
Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness  ...  Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling.  ...  Acknowledgments: The Authors wish to express gratitude to Fityanul Akhyar, and Syauki Aulia Thamrin, for their assistance.  ... 
doi:10.3390/app11094021 doaj:20d33d2cf0ec45c99ad373c371bc9a38 fatcat:w3u4btxnc5debiamyh4rd5wp4i

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  [141] also incorporated a GCN into a unified CNN architecture for 2D vessel segmentation on retinal image datasets.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

SUD: Supervision by Denoising for Medical Image Segmentation [article]

Sean I. Young, Adrian V. Dalca, Enzo Ferrante, Polina Golland, Bruce Fischl, Juan Eugenio Iglesias
2022 arXiv   pre-print
For many segmentation problems, however, data with pixel- or voxel-level labeling accuracy are scarce due to the cost of manual labeling.  ...  Training a fully convolutional network for semantic segmentation typically requires a large, labeled dataset with little label noise if good generalization is to be guaranteed.  ...  Support for this research was provided in part by the BRAIN Initiative Cell Census Network grant U01MH117023, the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 1R01EB023281  ... 
arXiv:2202.02952v1 fatcat:zuxk2jcnnnat3jrbqxv5wutftu

IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications

Zhanyu Ma, Sunwoo Kim, Pascual Martinez-Gomez, Jalil Taghia, Yi-Zhe Song, Huiji Gao
2020 IEEE Access  
With the annotated data, the authors develop the segmenter for the ancient Chinese medical texts.  ...  One-shot learning, which refers to the learning process with scarce data, is also explored and their approach shows notable performance.  ...  Based on the long short-term memory neural network (LSTM NN), they develop a data-driven trajectory model to generate human-like driving trajectories.  ... 
doi:10.1109/access.2020.3035461 fatcat:rt7ejtponrfexigie4cfpt7gd4

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis.  ...  computed tomography (CT) image patches with very scarce manual labels.  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

Chinthakindi Balaram Murthy, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, Zong Woo Geem
2020 Applied Sciences  
Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks.  ...  One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene.  ...  It is conceptually simple to train, flexible, and is a general framework for instance segmentation of objects.  ... 
doi:10.3390/app10093280 fatcat:e6jrltv6lrhxjntlhq7d34247e

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  [187] also incorporated a GCN into a unified CNN architecture for 2D vessel segmentation on retinal image datasets.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Deep Learning for Automated Medical Image Analysis [article]

Wentao Zhu
2019 arXiv   pre-print
Fourth, we will show how to use weakly labeled data to improve existing lung nodule detection system by integrating deep learning with a probabilistic graphic model.  ...  Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment.  ...  for medical image segmentation.  ... 
arXiv:1903.04711v1 fatcat:xigyugddlrentc42o5mnlbhdkq

FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom [article]

Hélène Lajous
2021 arXiv   pre-print
We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.  ...  Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth.  ...  We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University  ... 
arXiv:2109.03624v1 fatcat:rwaqc7vkyfcgte4xqirotjhrjq

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study [article]

Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
2020 arXiv   pre-print
The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data---a necessity for and pitfall of current supervised  ...  The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data.  ...  ACKNOWLEDGMENT The authors would like to thank their clinical partners at Klinikum rechts der Isar, Munich, for generously providing their data.  ... 
arXiv:2004.03271v2 fatcat:jkmlxvwno5echceheboqdmzo4u
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