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Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging.
Signature verification using a” siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Assessing the classification of liver focal lesions by using multi-phase computer tomography scans
Auréline Quatrehomme, Ingrid Millet, Denis Hoa, Gérard Subsol, and William Puech · 2013
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Multiple paragangliomas of head and neck associated with hepatic paraganglioma: a case report
Zebin Xiao, Dejun She, and Dairong Cao · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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A unified level set framework combining hybrid algorithms for liver and liver tumor segmentation in ct images
Zhou Zheng, Xuechang Zhang, Huafei Xu, Wang Liang, Siming Zheng, and Yueding Shi · 2018
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Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced ct: a preliminary study
Koichiro Yasaka, Hiroyuki Akai, Osamu Abe, and Shigeru Kiryu · 2018
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Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase ct images
Dong Liang, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Qingqing Chen, Xianhua Han, Yen-Wei Chen, et al · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
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Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
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Ct and mri liver imaging reporting and data system version 2018 for hepatocellular carcinoma: a systematic review with meta-analysis
Sunyoung Lee, Yeun-Yoon Kim, Jaeseung Shin, Shin Hye Hwang, Yun Ho Roh, Yong Eun Chung, and Jin-Young Choi · 2020
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Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists
Muhammad Attique Khan, Imran Ashraf, Majed Alhaisoni, Robertas Damaševičius, Rafal Scherer, Amjad Rehman, and Syed Ahmad Chan Bukhari · 2020
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Co-heterogeneous and adaptive segmentation from multi-source and multi-phase ct imaging data: A study on pathological liver and lesion segmentation
Ashwin Raju, Chi-Tung Cheng, Yuankai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHung Liao, and Adam P Harrison · 2020
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Multi-phase and multi-level selective feature fusion for automated pancreas segmentation from ct images
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Mscs-deepln: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks
Xiuyuan Xu, Chengdi Wang, Jixiang Guo, Yuncui Gan, Jianyong Wang, Hongli Bai, Lei Zhang, Weimin Li, and Zhang Yi · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Radiologic-pathologic correlation of hepatobiliary phase hypointense nodules without arterial phase hyperenhancement at gadoxetic acid–enhanced mri: a multicenter study
Ijin Joo, So Yeon Kim, Tae Wook Kang, Young Kon Kim, Beom Jin Park, Yoon Jin Lee, Joon-Il Choi, Chang-Hee Lee, Hee Sun Park, Kyoungbun Lee, et al · 2020
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2020
Bottleneck transformers for visual recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, and Ashish Vaswani · 2021
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Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced ct and clinical data
Ruitian Gao, Shuai Zhao, Kedeerya Aishanjiang, Hao Cai, Ting Wei, Yichi Zhang, Zhikun Liu, Jie Zhou, Bing Han, Jian Wang, et al · 2021
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Early convolutions help transformers see better
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Rare benign liver tumors that require differentiation from hepatocellular carcinoma: focus on diagnosis and treatment
Laihui Luo, Tao Wang, Mengting Cheng, Xian Ge, Shengjiang Song, Guoqing Zhu, Yongqiang Xiao, Wei Deng, Jin Xie, and Renfeng Shan · 2022
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M3net: A multi-scale multi-view framework for multi-phase pancreas segmentation based on cross-phase non-local attention
Taiping Qu, Xiheng Wang, Chaowei Fang, Li Mao, Juan Li, Ping Li, Jinrong Qu, Xiuli Li, Huadan Xue, Yizhou Yu, et al · 2022
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Cancer statistics for the year 2020: An overview
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
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Exploring simple siamese representation learning
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Deep metric learning for few-shot image classification: A review of recent developments
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Uniformer: Unifying convolution and self-attention for visual recognition
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nnformer: volumetric medical image segmentation via a 3d transformer
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H2former: An efficient hierarchical hybrid transformer for medical image segmentation
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Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
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