[1] JING B,XIE P,XING E.On the automatic generation of medical imaging reports[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.[S.l.]:Association for Computational Linguistics,2018:2577-2586. [2] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York,USA:ACM Press,2017:6000-6010. [3] DOSOVITSKIY A,BEYER L,KOLESNIKOV A,et al.An image is worth 16×16 words:transformers for image recognition at scale[EB/OL].[2022-5-20].https://www.xueshufan.com/publication/3119786062. [4] LIU Z,WANG Y,HAN K,et al.Post-training quantization for vision transformer[EB/OL].[2022-05-20].https://arxiv.org/abs/2106.14156. [5] YUAN Y,FU R,HUANG L,et al.HRFormer:high-resolution vision transformer for dense predict[EB/OL].[2022-5-20].https://openreview.net/forum?id=DF8LCjR03tX. [6] SNOEK C G M,WORRING M,SMEULDERS A W M.Early versus late fusion in semantic video analysis[C]//Proceedings of the 13th Annual ACM International Conference on Multimedia.New York,USA:ACM Press,2005:399-402. [7] CHAIB S,LIU H,GU Y F,et al.Deep feature fusion for VHR remote sensing scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(8):4775-4784. [8] DONG H,PAN J S,XIANG L,et al.Multi-scale boosted dehazing network with dense feature fusion[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2020:2154-2164. [9] LIU C J,WECHSLER H.A shape and texture based enhanced fisher classifier for face recognition[J].IEEE Transactions on Image Processing,2001,10(4):598-608. [10] YANG J,YANG J.Generalized K-L transform based combined feature extraction[J].Pattern Recognition,2002,35(1):295-297. [11] YANG J,YANG J,ZHANG D,et al.Feature fusion:parallel strategy vs.serial strategy[J].Pattern Recognition,2003,36(6):1369-1381. [12] LIU X W,ZHU X Z,LI M M,et al.Late fusion incomplete multi-view clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(10):2410-2423. [13] TRONG V H,GWANG-HYUN Y,VU D T,et al.Late fusion of multimodal deep neural networks for weeds classification[J].Computers and Electronics in Agriculture,2020,175(6):56-67. [14] 李娜,顾庆,姜枫,等.一种基于卷积神经网络的砂岩显微图像特征表示方法[J].软件学报,2020,31(11):3621-3639.LI N,GU Q,JIANG F,et al.Feature representation method of microscopic sandstone images based on convolutional neural network[J].Journal of Software,2020,31(11):3621-3639.(in Chinese) [15] 袁单飞,陈慈发,董方敏.基于多尺度分割的图像识别残差网络研究[J].计算机工程,2022,48(5):258-262,271.YUAN D F,CHEN C F,DONG F M.Research on residual network of image recognition based on multiscale split[J].Computer Engineering,2022,48(5):258-262,271.(in Chinese) [16] MATSOUKAS C,HASLUM J F,SÖDERBERG M,et al.Is it time to replace CNNs with transformers for medical images?[EB/OL].[2022-5-20].https://arxiv.org/abs/2108.09038. [17] VINYALS O,TOSHEV A,BENGIO S,et al.Show and tell:a neural image caption generator[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3156-3164. [18] NAJDENKOSKA I,ZHEN X T,WORRING M,et al.Variational topic inference for chest X-ray report generation[M]//BRUIJNE M D,CATTIN P C,COTIN S,et al.Medical image computing and computer assisted intervention.Berlin,Germany:Springer International Publishing,2021:625-635. [19] HAN Z,WEI B,XI X,et al.Unifying neural learning and symbolic reasoning for spinal medical report generation[J].Medical Image Analysis,2021,67:101872. [20] YANG X,GIREESH N,XING E,et al.XRayGAN:consistency-preserving generation of X-ray images from radiology reports[EB/OL].[2022-05-20].https://arxiv.org/abs/2006.10552. [21] HUANG J H,YANG C H H,LIU F Y,et al.DeepOpht:medical report generation for retinal images via deep models and visual explanation[C]//Proceedings of IEEE Winter Conference on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2021:2441-2451. [22] KRAUSE J,JOHNSON J,KRISHNA R,et al.A hierarchical approach for generating descriptive image paragraphs[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA.IEEE Press,2017:3337-3345. [23] LI C Y,LIANG X,HU Z,et al.Hybrid retrieval-generation reinforced agent for medical image report generation[EB/OL].[2022-05-20].https://arxiv.org/abs/1805.08298. [24] JING B Y,WANG Z Y,XING E.Show,describe and conclude:on exploiting the structure information of chest X-ray reports[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Stroudsburg,USA:Association for Computational Linguistics,2019:6570-6580. [25] ZHANG Y X,WANG X S,XU Z Y,et al. When radiology report generation meets knowledge graph[EB/OL].[2022-05-20]https://arxiv.org/abs/2002.08277v1. [26] XU W,QI C,XU Z,et al.Reinforced medical report generation with X-linear attention and repetition penalty[EB/OL].[2022-05-20].https://arxiv.org/abs/2011.07680. [27] CHEN Z H,SONG Y,CHANG T H,et al.Generating radiology reports via memory-driven transformer[C]//Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:Association for Computational Linguistics,2020:1439-1449. [28] LIU F L,WU X,GE S,et al.Exploring and distilling posterior and prior knowledge for radiology report generation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2021:13748-13757. [29] LIU F,YIN C,WU X,et al.Contrastive attention for automatic chest X-ray report generation[EB/OL].[2022-05-20].https://arxiv.org/abs/2106.06965. |