Fetching the paper…
Reading the bibliography…
Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination.
MIMIC-CXR: A large publicly available database of labeled chest radiographs
Johnson, A. E., Pollard, T. J., Berkowitz, S., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., and Horng, S. (2019) · 1901
Earlier work this paper cites.
Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Zhang, Y., Merck, D., Tsai, E. B., Manning, C. D., and Langlotz, C. P. (2019) · 1911
Earlier work this paper cites.
Measuring the accuracy of diagnostic systems
Swets, J. A. (1988) · 1988
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. (1992) · 1992
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
Earlier work this paper cites.
Building Natural Language Generation Systems
Reiter, E. and Dale, R. (2000) · 2000
Earlier work this paper cites.
BLEU: A method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y. (2004) · 2004
Earlier work this paper cites.
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., and Altun, Y. (2004) · 2004
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A. (2005) · 2005
Earlier work this paper cites.
Real versus template-based natural language generation: A false opposition?
Van Deemter, K., Krahmer, E., and Theune, M. (2005) · 2005
Earlier work this paper cites.
Accuracy of diagnostic procedures: Has it improved over the past five decades?
Berlin, L. (2007) · 2007
Earlier work this paper cites.
A software tool for removing patient identifying information from clinical documents
Friedlin, F. J. and McDonald, C. J. (2008) · 2008
Earlier work this paper cites.
Introduction to information retrieval
Manning, C. D., Raghavan, P., and Schütze, H. (2008) · 2008
Earlier work this paper cites.
Fundamentals of Medical Imaging
Suetens, P. (2009) · 2009
Earlier work this paper cites.
Current perspectives in medical image perception
Krupinski, E. A. (2010) · 2010
Earlier work this paper cites.
Machine learning: a probabilistic perspective
Murphy, K. P. (2012) · 2012
Earlier work this paper cites.
SemScribe: Natural language generation for medical reports
Varges, S., Bieler, H., Stede, M., Faulstich, L. C., Irsig, K., and Atalla, M. (2012) · 2012
Earlier work this paper cites.
The NLM medical text indexer system for indexing biomedical literature
Mork, J. G., Jimeno-Yepes, A., and Aronson, A. R. (2013) · 2013
Earlier work this paper cites.
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
DenseNet: Implementing efficient ConvNet descriptor pyramids
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K. (2014) · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014) · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Earlier work this paper cites.
Microsoft COCO captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L. (2015) · 2015
Earlier work this paper cites.
Diagnostic radiology resident and fellow workloads: A 12-year longitudinal trend analysis using national medicare aggregate claims data
Chokshi, F. H., Hughes, D. R., Wang, J. M., Mullins, M. E., Hawkins, C. M., and Duszak Jr, R. (2015) · 2015
Earlier work this paper cites.
Preparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., and McDonald, C. J. (2015) · 2015
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T. (2015) · 2015
Earlier work this paper cites.
Re-evaluating automatic summarization with BLEU and 192 shades of ROUGE
Graham, Y. (2015) · 2015
Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
Karpathy, A. and Fei-Fei, L. (2015) · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
Cited alongside, same era.
Predicting semantic descriptions from medical images with convolutional neural networks
Schlegl, T., Waldstein, S. M., Vogl, W.-D., Schmidt-Erfurth, U., and Langs, G. (2015) · 2015
Cited alongside, same era.
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, Z. C. L., and Parikh, D. (2015) · 2015
Cited alongside, same era.
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015) · 2015
Cited alongside, same era.
Show and tell: Lessons learned from the 2015 MSCOCO image captioning challenge
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2017) · 2015
Cited alongside, same era.
Hybrid retrieval-generation reinforced agent for medical image report generation
Li, Y., Liang, X., Hu, Z., and Xing, E. (2018) · 2018
Later among the works it cites.
The future of radiology augmented with artificial intelligence: A strategy for success
Liew, C. (2018) · 2018
Later among the works it cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Sharma, P., Ding, N., Goodman, S., and Soricut, R. (2018) · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
Later among the works it cites.
TieNet: Text-image embedding network for common thorax disease classification and reporting in chest X-rays
Wang, X., Peng, Y., Lu, L., Lu, Z., and Summers, R. M. (2018) · 2018
Later among the works it cites.
Multimodal recurrent model with attention for automated radiology report generation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
SPICE: Semantic propositional image caption evaluation
Anderson, P., Fernando, B., Johnson, M., and Gould, S. (2016) · 2016
Cited alongside, same era.
Automatic description generation from images: A survey of models, datasets, and evaluation measures
Bernardi, R., Cakici, R., Elliott, D., Erdem, A., Erdem, E., Ikizler-Cinbis, N., Keller, F., Muscat, A., and Plank, B. (2016) · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Re-evaluating automatic metrics for image captioning
Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., and Erdem, E. (2016) · 2016
Cited alongside, same era.
Medical image captioning: Learning to describe medical image findings using multi-task-loss CNN
Kisilev, P., Sason, E., Barkan, E., and Hashoul, S. (2016) · 2016
Cited alongside, same era.
Xue, Y., Xu, T., Long, L. R., Xue, Z., Antani, S., Thoma, G. R., and Huang, X. (2018) · 2018
Later among the works it cites.
ImageSem at ImageCLEF 2018 caption task: Image retrieval and transfer learning
Zhang, Y., Wang, X., Guo, Z., and Li, J. (2018) · 2018
Later among the works it cites.
nocaps: novel object captioning at scale
Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., and Anderson, P. (2019) · 2019
Later among the works it cites.
A comprehensive survey of deep learning for image captioning
Hossain, M., Sohel, F., Shiratuddin, M. F., and Laga, H. (2019) · 2019
Later among the works it cites.
Multi-attention and incorporating background information model for chest X-ray image report generation
Huang, X., Yan, F., Xu, W., and Li, M. (2019) · 2019
Later among the works it cites.
CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · 2019
Later among the works it cites.
A Survey on Biomedical Image Captioning
Kougia, V., Pavlopoulos, J., and Androutsopoulos, I. (2019) · 2019
Later among the works it cites.
Knowledge-driven encode, retrieve, paraphrase for medical image report generation
Li, Y., Liang, X., Hu, Z., and Xing, E. (2019) · 2019
Later among the works it cites.
Overview of the ImageCLEFmed 2019 Concept Prediction Task
Pelka, O., Friedrich, C. M., de Herrera, A. G. S., and Müller, H. (2019) · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
Later among the works it cites.
Sumqe: a bert-based summary quality estimation model
Xenouleas, S., Malakasiotis, P., Apidianaki, M., and Androutsopoulos, I. (2019) · 2019
Later among the works it cites.
Automatic generation of medical imaging diagnostic report with hierarchical recurrent neural network
Yin, C., Qian, B., Wei, J., Li, X., Zhang, X., Li, Y., and Zheng, Q. (2019) · 2019
Later among the works it cites.
Automatic radiology report generation based on multi-view image fusion and medical concept enrichment
Yuan, J., Liao, H., Luo, R., and Luo, J. (2019) · 2019
Later among the works it cites.
Baselines for chest x-ray report generation
Boag, W., Hsu, T.-M. H., McDermott, M., Berner, G., Alesentzer, E., and Szolovits, P. (2020) · 2020
Later among the works it cites.
Padchest: A large chest x-ray image dataset with multi-label annotated reports
Bustos, A., Pertusa, A., Salinas, J.-M., and de la Iglesia-Vayá, M. (2020) · 2020
Later among the works it cites.
Generating radiology reports via memory-driven transformer
Chen, Z., Song, Y., Chang, T.-H., and Wan, X. (2020) · 2020
Later among the works it cites.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Kassner, N. and Schütze, H. (2020) · 2020
Later among the works it cites.
Generalization through memorization: Nearest neighbor language models
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., and Lewis, M. (2020) · 2020
Later among the works it cites.
Deep learning in generating radiology reports: A survey
Monshi, M. M. A., Poon, J., and Chung, V. (2020) · 2020
Later among the works it cites.
Image captioning using facial expression and attention
Nezami, O. M., Dras, M., Wan, S., and Paris, C. (2020) · 2020
Later among the works it cites.
Overview of the imageclefmed 2020 concept prediction task: medical image understanding
Pelka, O., Friedrich, C. M., Garcıa Seco de Herrera, A., and Müller, H. (2020) · 2020
Later among the works it cites.
Bleurt: Learning robust metrics for text generation
Sellam, T., Das, D., and Parikh, A. P. (2020) · 2020
Later among the works it cites.
Are we estimating or guesstimating translation quality?
Sun, S., Guzmán, F., and Specia, L. (2020) · 2020
Later among the works it cites.
Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015) · 2057
Closest in time.