Fetching the paper…
Reading the bibliography…
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability.
Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, and M. Chen, “Medical image classification with convolutional neural network,” in 2014 13th international conference on control automation robotics & vision (ICARCV)
2014
Earlier work this paper cites.
W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, “Multi-scale convolutional neural networks for lung nodule classification,” in International conference on information processing in medical imaging
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Qu, N. Hiruta, K. Terai, H. Nosato, M. Murakawa, and H. Sakanashi, “Gastric pathology image classification using stepwise fine-tuning for deep neural networks,” Journal of healthcare engineering
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
G. Murtaza, L. Shuib, A. W. Abdul Wahab, G. Mujtaba, G. Mujtaba, H. F. Nweke, M. A. Al-garadi, F. Zulfiqar, G. Raza, and N. A. Azmi, “Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges,” Artificial Intelligence Review
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al
2020
Earlier work this paper cites.
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, “Biobert: a pre-trained biomedical language representation model for biomedical text mining,” Bioinformatics
2020
Earlier work this paper cites.
M. Chen, B. Zhang, W. Topatana, J. Cao, H. Zhu, S. Juengpanich, Q. Mao, H. Yu, and X. Cai, “Classification and mutation prediction based on histopathology h&e images in liver cancer using deep learning,” NPJ precision oncology
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Cited alongside, same era.
J. Shao, S. Chen, Y. Li, K. Wang, Z. Yin, Y. He, J. Teng, Q. Sun, M. Gao, J. Liu, et al
J. Chen, H. Guo, K. Yi, B. Li, and M. Elhoseiny, “Visualgpt: Data-efficient adaptation of pretrained language models for image captioning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2022
Later among the works it cites.
E. Tiu, E. Talius, P. Patel, C. P. Langlotz, A. Y. Ng, and P. Rajpurkar, “Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning,” Nature Biomedical Engineering
2022
Later among the works it cites.
X. Zhai, X. Wang, B. Mustafa, A. Steiner, D. Keysers, A. Kolesnikov, and L. Beyer, “Lit: Zero-shot transfer with locked-image text tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
C.-L. Chen, C.-C. Chen, W.-H. Yu, S.-H. Chen, Y.-C. Chang, T.-I. Hsu, M. Hsiao, C.-Y. Yeh, and C.-Y. Chen, “An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning,” Nature communications
2021
Cited alongside, same era.
M. Y. Lu, D. F. Williamson, T. Y. Chen, R. J. Chen, M. Barbieri, and F. Mahmood, “Data-efficient and weakly supervised computational pathology on whole-slide images,” Nature biomedical engineering
2021
Cited alongside, same era.
Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang, “Meta-baseline: Exploring simple meta-learning for few-shot learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
Cited alongside, same era.
K. Ding, M. Zhou, H. Wang, S. Zhang, and D. N. Metaxas, “Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study,” The Lancet Digital Health
2022
Cited alongside, same era.
M. Yasunaga, J. Leskovec, and P. Liang, “Linkbert: Pretraining language models with document links,” in Association for Computational Linguistics (ACL)
2022
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
H. Liu, K. Son, J. Yang, C. Liu, J. Gao, Y. J. Lee, and C. Li, “Learning customized visual models with retrieval-augmented knowledge,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Closest in time.
K. Ding, M. Zhou, H. Wang, O. Gevaert, D. Metaxas, and S. Zhang, “A large-scale synthetic pathological dataset for deep learning-enabled segmentation of breast cancer,” Scientific Data
2023
Closest in time.