E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model

An image is worth 16x16 words: Transformers for image recognition at scale

Original

Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010

Earlier work this paper cites.

Lora: Low-rank adaptation of large language models

Original

Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021

Earlier work this paper cites.

Learning transferable visual models from natural language supervision

Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021

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Mlp-mixer: An all-mlp architecture for vision

Tolstikhin, I. O.; Houlsby, N.; Kolesnikov, A.; Beyer, L.; Zhai, X.; Unterthiner, T.; Yung, J.; Steiner, A.; Keysers, D.; Uszkoreit, J.; et al. 2021 · 2021

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Multi-modal masked autoencoders for medical vision-and-language pre-training

Chen, Z.; Du, Y.; Hu, J.; Liu, Y.; Li, G.; Wan, X.; and Chang, T.-H. 2022 · 2022

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Masked autoencoders are scalable vision learners

He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022

Earlier work this paper cites.

Gpt-4 technical report

Original

Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023

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Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality

Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; and Xing, E. P. 2023 · 2023

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Med-flamingo: a multimodal medical few-shot learner

Moor, M.; Huang, Q.; Wu, S.; Yasunaga, M.; Dalmia, Y.; Leskovec, J.; Zakka, C.; Reis, E. P.; and Rajpurkar, P. 2023 · 2023

Cited alongside, same era.

Gemini: a family of highly capable multimodal models

Original

Team, G.; Anil, R.; Borgeaud, S.; Wu, Y.; Alayrac, J.-B.; Yu, J.; Soricut, R.; Schalkwyk, J.; Dai, A. M.; Hauth, A.; et al. 2023 · 2023

Cited alongside, same era.

Xraygpt: Chest radiographs summarization using medical vision-language models

Original

Thawkar, O.; Shaker, A.; Mullappilly, S. S.; Cholakkal, H.; Anwer, R. M.; Khan, S.; Laaksonen, J.; and Khan, F. S. 2023 · 2023

Cited alongside, same era.

Llama: Open and efficient foundation language models

Original

Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023

Cited alongside, same era.

Towards generalist foundation model for radiology

A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities

Original

Hamamci, I. E.; Er, S.; Almas, F.; Simsek, A. G.; Esirgun, S. N.; Dogan, I.; Dasdelen, M. F.; Wittmann, B.; Simsar, E.; Simsar, M.; et al. 2024 · 2024

Closest in time.

Llava-med: Training a large language-and-vision assistant for biomedicine in one day

Li, C.; Wong, C.; Zhang, S.; Usuyama, N.; Liu, H.; Yang, J.; Naumann, T.; Poon, H.; and Gao, J. 2024 · 2024

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Vila: On pre-training for visual language models

Lin, J.; Yin, H.; Ping, W.; Molchanov, P.; Shoeybi, M.; and Han, S. 2024 · 2024

Closest in time.

Benchmarking and Boosting Radiology Report Generation for 3D High-Resolution Medical Images

Original

Liu, C.; Wan, Z.; Wang, Y.; Shen, H.; Wang, H.; Zheng, K.; Zhang, M.; and Arcucci, R. 2024 · 2024

Closest in time.

STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical