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Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19.
“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger et al., · 2015
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“Grad-cam: Visual explanations from deep networks via gradient-based localization,”
Ramprasaath R Selvaraju et al., · 2017
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“Hyperdense-net: a hyper-densely connected cnn for multi-modal image segmentation,”
Jose Dolz, Karthik Gopinath, et al., · 2018
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“Attention u-net: Learning where to look for the pancreas,”
Ozan Oktay, Jo Schlemper, et al., · 2018
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“Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,”
Jiasen Lu, Dhruv Batra, et al., · 2019
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“Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,”
Zongwei Zhou et al., · 2019
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“Denouements of machine learning and multimodal diagnostic classification of alzheimer’s disease,”
Binny Naik et al., · 2020
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“Covid_mtnet: Covid-19 detection with multi-task deep learning approaches,”
Md Zahangir Alom, MM Rahman, Mst Shamima Nasrin, Tarek M Taha, and Vijayan K Asari, · 2020
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“Mosmeddata: Chest ct scans with covid-19 related findings dataset,”
Sergey P Morozov, AE Andreychenko, et al., · 2020
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http://medicalsegmentation.com/covid19/ Accessed December 23, 2020
“Covid-19 ct segmentation dataset,” [EB/OL], · 2020
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“Multimodal spatial attention module for targeting multimodal pet-ct lung tumor segmentation,”
Xiaohang Fu, Lei Bi, et al., · 2021
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“Learning transferable visual models from natural language supervision,”
Alec Radford, Jong Wook Kim, et al., · 2021
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“Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic,”
Sneha Kugunavar et al., · 2021
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“Covid tv-unet: Segmenting covid-19 chest ct images using connectivity imposed unet,”
Narges Saeedizadeh et al., · 2021
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“Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition,”
“Transunet: Transformers make strong encoders for medical image segmentation,”
Jieneng Chen, Yongyi Lu, Qihang Yu, et al., · 2021
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“Swin-unet: Unet-like pure transformer for medical image segmentation,”
Hu Cao, Yueyue Wang, Joy Chen, et al., · 2021
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“Lvit: language meets vision transformer in medical image segmentation,”
Zihan Li et al., · 2022
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“Tganet: Text-guided attention for improved polyp segmentation,”
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, et al., · 2022
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“Mrdff: A deep forest based framework for ct whole heart segmentation,”
Fei Xu et al., · 2022
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Shih-Cheng Huang, Liyue Shen, et al., · 2021
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“Convolutional sparse support estimator-based covid-19 recognition from x-ray images,”
Mehmet Yamac et al., · 2021
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“nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,”
Fabian Isensee, Paul F Jaeger, et al., · 2021
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“Tfcns: A cnn-transformer hybrid network for medical image segmentation,”
Zihan Li et al., · 2022
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Zihan Li et al., · 2022
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“Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer,”
Haonan Wang, Peng Cao, et al., · 2022
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