🔥🔥This is a collection of awesome articles about Transformer models in the medical imaging🔥🔥
📢 Our review paper published on arXiv: Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review ❤️
@article{azad2023advances,
title={Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review},
author={Azad, Reza and Kazerouni, Amirhossein and Heidari, Moein and Aghdam, Ehsan Khodapanah and Molaei, Amirali and Jia, Yiwei and Jose, Abin and Roy, Rijo and Merhof, Dorit},
journal={arXiv preprint arXiv:2301.03505},
year={2023}
}
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
Sachin Mehta, Ximing Lu, Donald Weaver, Joann G. Elmore, Hannaneh Hajishirzi, Linda Shapiro
[25th Jul, 2020] [MedIA Journal, 2022]
[PDF] [GitHub]
A graph-transformer for whole slide image classification
Yi Zheng, Rushin H. Gindra, Emily J. Green, Eric J. Burks, Margrit Betke, Jennifer E. Beane, Vijaya B. Kolachalama
[19th May, 2022] [TMI Journal, 2022]
[PDF] [GitHub]
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification
Moinak Bhattacharya, Shubham Jain, Prateek Prasanna
[23rd Feb., 2022] [ECCV, 2022]
[PDF]
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye
[2nd Nov., 2021] [NeurIPS, 2021]
[PDF]
Vision transformer for classification of breast ultrasound images
Behnaz Gheflati, Hassan Rivaz
[27th Oct., 2021] [EMBC, 2022]
[PDF]
MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification
Shuang Yu, Kai Ma, Qi Bi, Cheng Bian, Munan Ning, Nanjun He, Yuexiang Li, Hanruo Liu, Yefeng Zheng
[21st Sep., 2021] [MICCAI, 2021]
[PDF] [GitHub]
3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment
Sheng Wang, Zixu Zhuang, Kai Xuan, Dahong Qian, Zhong Xue, Jia Xu, Ying Liu, Yiming Chai, Lichi Zhang, Qian Wang, Dinggang Shen
[21st Sep., 2021] [MICCAI Workshop, 2021]
[PDF]
COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
Debaditya Shome, T. Kar, Sachi Nandan Mohanty, Prayag Tiwari, Khan Muhammad, Abdullah AlTameem, Yazhou Zhang, Abdul Khader Jilani Saudagar
[23rd Sep., 2021] [International Journal of Environmental Research and Public Health, 2021]
[PDF] [GitHub]
Is it Time to Replace CNNs with Transformers for Medical Images?
Christos Matsoukas, Johan Fredin Haslum, Magnus Söderberg, Kevin Smith
[20th Aug., 2021] [ICCV Workshop, 2021]
[PDF] [GitHub]
Vision Transformer for femur fracture classification
Leonardo Tanzi, Andrea Audisio, Giansalvo Cirrincione, Alessandro Aprato, Enrico Vezzetti
[7th Aug., 2021] [Injury Journal, 2022]
[PDF]
xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography
Arnab Kumar Mondal, Arnab Bhattacharjee, Parag Singla, A. P. Prathosh
[7th Jul., 2021] [IEEE Journal of Translational Engineering in Health and Medicine, 2021]
[PDF] [Github]
COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models
Xiaohong Gao, Yu Qian, Alice Gao
[4th Jul., 2021] [NextComp, 2022]
[PDF] [GitHub]
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, Yongbing Zhang
[2nd Jun., 2021] [NeurIPS, 2021]
[PDF] [GitHub]
Lesion-Aware Transformers for Diabetic Retinopathy Grading
Rui Sun, Yihao Li, Tianzhu Zhang, Zhendong Mao, Feng Wu, Yongdong Zhang
[1st Jun., 2021] [CVPR, 2021]
[PDF]
POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound
Shehan Perera, Srikar Adhikari, Alper Yilmaz
[20th May, 2021] [ICIP, 2022]
[PDF]
Automatic diagnosis of covid-19 using a tailored transformer-like network
Chengeng Liu, Qingshan Yin
[21st Apr., 2021] [CISAT, 2021]
[PDF]
Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus
Sangjoon Park, Gwanghyun Kim, Yujin Oh, Joon Beom Seo, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Jong Chul Ye
[12th Mar., 2021] [arXiv, 2021]
[PDF]
TransMed: Transformers Advance Multi-modal Medical Image Classification
Yin Dai, Yifan Gao
[10th Mar., 2021] [Diagnostics, 2021]
[PDF]
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Reza Azad, Moein Heidari, Moein Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, Ehsan Adeli, Dorit Merhof
[1st Aug., 2022] [MICCAI Workshop, 2022]
[PDF] [GitHub]
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah, Aghdam Julien Cohen-Adad, Dorit Merhof
[18th Jul., 2022] [WACV, 2023]
[PDF] [GitHub]
Self Pre-training with Masked Autoencoders for Medical Image Analysis
Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna
[10th Mar., 2022] [arXiv, 2022]
[PDF]
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu
[4th Jan., 2022] [MICCAI Workshop]
[PDF] [GitHub]
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer
Xiangde Luo, Minhao Hu, Tao Song, Guotai Wang, Shaoting Zhang
[9th Dec., 2021] [MIDL, 2022]
[PDF] [Github]
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Dong Yang, Andriy Myronenko, Xiaosong Wang, Ziyue Xu, Holger R. Roth, Daguang Xu
[15th Nov., 2021] [ICCV, 2021]
[PDF]
MISSFormer: An Effective Medical Image Segmentation Transformer
Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan
[15th Sep., 2021] [TMI Journal, 2022]
[PDF] [GitHub]
nnFormer: Interleaved Transformer for Volumetric Segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu
[7th Sep., 2021] [arXiv, 2021]
[PDF] [GitHub]
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, Rick Goh
[20th May, 2021] [IJCAI, 2021]
[PDF] [GitHub]
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, Manning Wang
[12th May, 2021] [arXiv, 2021]
[PDF] [GitHub]
UNETR: Transformers for 3D Medical Image Segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, Daguang Xu
[18th Mar., 2021] [WACV, 2022]
[PDF] [GitHub]
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
Jiangyun Li, Wenxuan Wang, Chen Chen, Tianxiang Zhang, Sen Zha, Hong Yu, Jing Wang
[7th Mar, 2021] [MICCAI, 2021]
[PDF] [GitHub]
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Chunhua Shen, Yong Xia
[4th Mar., 2021] [MICCAI, 2021]
[PDF] [GitHub]
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, Vishal M. Patel
[21th Feb., 2021] [MICCAI, 2021]
[PDF] [GitHub]
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Yundong Zhang, Huiye Liu, Qiang Hu
[16th Feb., 2021] [arXiv, 2021]
[PDF] [GitHub]
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou
[8th Feb., 2021] [arXiv, 2021]
[PDF] [GitHub]
TransCT: Dual-path Transformer for Low Dose Computed Tomography
Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, Lei Xing
[28th Feb., 2021] [MICCAI, 2021]
[PDF] [GitHub]
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
Dayang Wang, Zhan Wu, Hengyong Yu
[8th Jun., 2021] [MICCAI Workshop, 2021]
[PDF] [GitHub]
Eformer: Edge Enhancement based Transformer for Medical Image Denoising
Achleshwar Luthra, Harsh Sulakhe, Tanish Mittal, Abhishek Iyer, Santosh Yadav
[16th Sep., 2021] [arXiv, 2021]
[PDF]
3D Transformer-GAN for High-Quality PET Reconstruction
Yanmei Luo, Yan Wang, Chen Zu, Bo Zhan, Xi Wu, Jiliu Zhou, Dinggang Shen, Luping Zhou
[21st Sep., 2021] [MICCAI, 2021]
[PDF]
Spatial Adaptive and Transformer Fusion Network (STFNet) for Low-count PET Blind Denoising with MRI
Lipei Zhang, Zizheng Xiao, Chao Zhou, Jianmin Yuan, Qiang He, Yongfeng Yang, Xin Liu, Dong Liang, Hairong Zheng, Wei Fan, Xu Zhang, Zhanli Hu
[19th Nov., 2021] [Medical Physics, 2021]
[PDF]
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising
Dayang Wang, Fenglei Fan, Zhan Wu, Rui Liu, Fei Wang, Hengyong Yu
[28th Feb., 2022] [arXiv, 2022]
[PDF] [GitHub]
Low-Dose CT Denoising via Sinogram Inner-Structure Transformer
Liutao Yang, Zhongnian Li, Rongjun Ge, Junyong Zhao, Haipeng Si, Daoqiang Zhang
[7th Apr., 2022] [IEEE Transactions on Medical Imaging, 2022]
[PDF]
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, S. Kevin Zhou
[21th Nov., 2021] [arXiv, 2021]
[PDF] [GitHub]
Fourier Image Transformer
Tim-Oliver Buchholz, Florian Jug
[6th Apr., 2021] [CVPR, 2022]
[PDF] [GitHub]
Dual-domain sparse-view CT reconstruction with Transformers
Changrong Shi, Yongshun Xiao, Zhiqiang Chen
[22nd Mar., 2022] [ELSEVIER Physica Medica, 2022]
[PDF]
Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View Cone-Beam CT Reconstruction
Minghui Wu, Yangdi Xu, Yingying Xu, Guangwei Wu, Qingqing Chen, Hongxiang Lin
[23th Mar., 2022] [arXiv, 2022]
[PDF]
Vision Transformers Enable Fast and Robust Accelerated MRI
Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, Yong Xu
[10th Dec., 2021] [MIDL, 2022]
[PDF] [GitHub]
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, Yong Xu
[12th Jun., 2021] [MICCAI, 2021]
[PDF] [GitHub]
MR Image Super Resolution By Combining Feature Disentanglement CNNs and Vision Transformers
Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, Yong Xu
[9th Dec., 2021] [MIDL, 2022]
[PDF]
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
Chaowei Fang, Dingwen Zhang, Liang Wang, Yulun Zhang, Lechao Cheng, Junwei Han
[29th Mar., 2022] [ACM MM, 2022]
[PDF]
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
Jiang Liu, Srivathsa Pasumarthi, Ben Duffy, Enhao Gong, Greg Zaharchuk, Keshav Datta
[28th Apr., 2022] [arXiv, 2021]
[PDF]
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation
Nicolae-Catalin Ristea, Andreea-Iuliana Miron, Olivian Savencu, Mariana-Iuliana Georgescu, Nicolae Verga, Fahad Shahbaz Khan, Radu Tudor Ionescu
[12th Oct., 2021] [arXiv, 2021]
[PDF] [GitHub]
ResViT: Residual vision transformers for multi-modal medical image synthesis
Onat Dalmaz, Mahmut Yurt, Tolga Çukur
[30th Jun., 2021] [TMI Journal, 2021]
[PDF] [GitHub]
PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
Xuzhe Zhang, Xinzi He, Jia Guo, Nabil Ettehadi, Natalie Aw, David Semanek, Jonathan Posner, Andrew Laine, Yun Wang
[28th May., 2021] [arXiv, 2021]
[PDF] [GitHub]
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers
Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod, Salah A. Baker
[14th Apr., 2021] [ICCV Workshop, 2021]
[PDF] [GitHub]
Focused Decoding Enables 3D Anatomical Detection by Transformers
Bastian Wittmann, Fernando Navarro, Suprosanna Shit, Bjoern Menze
[21st Jul., 2022] [arXiv, 2022]
[PDF] [GitHub]
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection
Royden Wagner, Karl Rohr
[1st Jun., 2022] [MIUA, 2022]
[PDF] [Github]
CT-CAD: Context-Aware Transformers for End-to-End Chest Abnormality Detection on X-Rays
Qiran Kong, Yirui Wu, Chi Yuan, Yongli Wang
[9th Dec., 2021] [BIBM, 2021]
[PDF]
RDFNet: A Fast Caries Detection Method Incorporating Transformer Mechanism
Hao Jiang, Peiliang Zhang, Chao Che, Bo Jin
[10th Nov., 2021] [Computational and Mathematical Methods in Medicine Journal, 2021]
[PDF]
Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers
Rong Tao, Guoyan Zheng
[21st Sep., 2021] [MICCAI, 2021]
[PDF]
Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries
Xinghua Ma, Gongning Luo, Wei Wang, Kuanquan Wang
[7th Jul., 2021] [MICCAI, 2021]
[PDF] [Github]
COTR: Convolution in Transformer Network for End to End Polyp Detection
Zhiqiang Shen, Chaonan Lin, Shaohua Zheng
[23rd May, 2021] [ICCC, 2021]
[PDF]
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI.
Junshen Xu, Daniel Moyer, P. Ellen Grant, Polina Golland, Juan Eugenio Iglesias, Elfar Adalsteinsson.
[22th Jun., 2022] [MICCAI, 2022]
[PDF] [GitHub]
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
Jiacheng Shi, Yuting He, Youyong Kong, Jean-Louis Coatrieux, Huazhong Shu, Guanyu Yang, Shuo Li
[15th Jun., 2022] [MICCAI, 2022]
[PDF] [GitHub]
TransMorph: Transformer for unsupervised medical image registration
Junyu Chen, Eric C. Frey, Yufan He, William P. Segars, Ye Li, Yong Du
[19th Nov., 2021] [MedIA Journal]
[PDF] [GitHub]
Learning dual transformer network for diffeomorphic registration
Yungeng Zhang, Yuru Pei & Hongbin Zha
[21th Sep., 2021] [MICCAI, 2021]
[PDF]
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
Junyu Chen, Yufan He, Eric C. Frey, Ye Li, Yong Du
[13th Apr., 2021] [MIDL, 2021]
[PDF] [GitHub]
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
Tony C. W. Mok, Albert C. S. Chung
[29th Mar., 2022] [CVPR, 2022]
[PDF] [GitHub]
Cross-modal Memory Networks for Radiology Report Generation
Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan
[28th Apr., 2022] [ACL-IJCNLP, 2021]
[PDF] [GitHub]
AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation
Di You, Fenglin Liu, Shen Ge, Xiaoxia Xie, Jing Zhang, Xian Wu
[18th Mar., 2022] [MICCAI, 2021]
[PDF]
Automated Generation of Accurate & Fluent Medical X-ray Reports
Hoang T.N. Nguyen, Dong Nie, Taivanbat Badamdorj, Yujie Liu, Yingying Zhu, Jason Truong, Li Cheng
[27th Aug., 2021] [EMNLP, 2021]
[PDF] [GitHub]
Medical-vlbert: Medical visual language bert for covid-19 ct report generation with alternate learning
Guangyi Liu, Yinghong Liao, Fuyu Wang, Bin Zhang, Lu Zhang, Xiaodan Liang, Xiang Wan, Shaolin Li, Zhen Li, Shuixing Zhang, Shuguang Cui
[11th Aug., 2021] [IEEE Transactions on Neural Networks and Learning Systems, 2021]
[PDF]
Surgical Instruction Generation with Transformers
Jinglu Zhang, Yinyu Nie, Jian Chang, Jian Jun Zhang
[14th Jul., 2021] [MICCAI, 2021]
[PDF]
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao Shi, Yang Zhang, Jianping Fan, Zhiqiang He
[21st Jun., 2021] [arXiv , 2021]
[PDF]
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation
Fenglin Liu, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou
[13th Jun., 2021] [CVPR, 2021]
[PDF]
Progressive Transformer-Based Generation of Radiology Reports
Farhad Nooralahzadeh, Nicolas Perez Gonzalez, Thomas Frauenfelder, Koji Fujimoto, Michael Krauthammer
[19th Feb., 2021] [EMNLP , 2021]
[PDF] [GitHub]
Learning to Generate Clinically Coherent Chest X-Ray Reports
Justin Lovelace, Bobak Mortazavi
[1st Nov., 2020] [EMNLP, 2020]
[PDF] [GitHub]
Generating Radiology Reports via Memory-driven Transformer
Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan
[30th Oct., 2020] [EMNLP, 2020]
[PDF] [GitHub]
Reinforced Transformer for Medical Image Captioning
Yuxuan Xiong, Bo Du, Pingkun Yan
[10th Oct., 2019][MICCAI Workshop, 2019]
[PDF]
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing
[25th Mar., 2019] [AAAI, 2019]
[PDF]