Fetching the paper…
Reading the bibliography…
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across diverse tasks, garnering significant attention in AI communities.
J. J. Lau, S. Gayen, A. Ben Abacha, and D. Demner-Fushman, “A dataset of clinically generated visual questions and answers about radiology images,” Scientific data , vol. 5, no. 1, pp. 1–10, 2018
2018
Earlier work this paper cites.
2020
Earlier work this paper cites.
E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “Sars-cov-2 ct-scan dataset: A large dataset of real patients ct scans for sars-cov-2 identification,” MedRxiv , pp. 2020–04, 2020
2020
Earlier work this paper cites.
E. Tjoa and C. Guan, “A survey on explainable artificial intelligence (xai): Toward medical xai,” IEEE transactions on neural networks and learning systems , vol. 32, no. 11, pp. 4793–4813, 2020
2020
Earlier work this paper cites.
B. Liu, L.-M. Zhan, L. Xu, L. Ma, Y. Yang, and X.-M. Wu, “Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2021, pp. 1650–1654
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” Advances in neural information processing systems , vol. 35, pp. 23 716–23 736, 2022
2022
Earlier work this paper cites.
Y. Nan, J. Del Ser, S. Walsh, C. Schönlieb, M. Roberts, I. Selby, K. Howard, J. Owen, J. Neville, J. Guiot et al. , “Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions,” Information Fusion , vol. 82, pp. 99–122, 2022
2022
Earlier work this paper cites.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
Earlier work this paper cites.
M. Moor, Q. Huang, S. Wu, M. Yasunaga, Y. Dalmia, J. Leskovec, C. Zakka, E. P. Reis, and P. Rajpurkar, “Med-flamingo: a multimodal medical few-shot learner,” in Machine Learning for Health (ML4H) . PMLR, 2023, pp. 353–367
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Wasserthal, H.-C. Breit, M. T. Meyer, M. Pradella, D. Hinck, A. W. Sauter, T. Heye, D. T. Boll, J. Cyriac, S. Yang et al. , “Totalsegmentator: robust segmentation of 104 anatomic structures in ct images,” Radiology: Artificial Intelligence , vol. 5, no. 5, 2023
2023
Cited alongside, same era.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, 2024
2024
Closest in time.
Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu et al. , “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 24 185–24 198
2024
Closest in time.
C. Li, C. Wong, S. Zhang, N. Usuyama, H. Liu, J. Yang, T. Naumann, H. Poon, and J. Gao, “Llava-med: Training a large language-and-vision assistant for biomedicine in one day,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Xing, G. Papanastasiou, S. Walsh, and G. Yang, “Less is more: unsupervised mask-guided annotated ct image synthesis with minimum manual segmentations,” IEEE Transactions on Medical Imaging , vol. 42, no. 9, pp. 2566–2576, 2023
2023
Cited alongside, same era.
W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. E. Gonzalez, H. Zhang, and I. Stoica, “Efficient memory management for large language model serving with pagedattention,” in Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023
2023
Cited alongside, same era.
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl et al. , “Large language models encode clinical knowledge,” Nature , vol. 620, no. 7972, pp. 172–180, 2023
2023
Cited alongside, same era.
K. Cortiñas-Lorenzo and G. Lacey, “Toward explainable affective computing: A review,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Cited alongside, same era.
Y. Chen, W. Lu, X. Qin, J. Wang, and X. Xie, “Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Cited alongside, same era.
Y. Hu, T. Li, Q. Lu, W. Shao, J. He, Y. Qiao, and P. Luo, “Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 22 170–22 183
2024
Cited alongside, same era.
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al. , “Judging llm-as-a-judge with mt-bench and chatbot arena,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.