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The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery.
“Data-driven visual tracking in retinal microsurgery”
Raphael Sznitman et al · 2012
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“Fast and accurate deep network learning by exponential linear units (elus)”
Djork-Arné Clevert, Thomas Unterthiner and Sepp Hochreiter · 2015
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Jimmy Ba, Jamie Kiros and Geoffrey Hinton · 2016
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“Endonet: a deep architecture for recognition tasks on laparoscopic videos”
Andru Twinanda et al · 2016
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“CATARACTS: Challenge on automatic tool annotation for cataRACT surgery”
Hassan Al et al · 2019
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“Efficientnet: Rethinking model scaling for convolutional neural networks”
Mingxing Tan and Quoc Le · 2019
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“2018 robotic scene segmentation challenge”
Max Allan et al · 2020
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“Tecno: Surgical phase recognition with multi-stage temporal convolutional networks”
Tobias Czempiel et al · 2020
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“Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80”
W-Y Hong et al · 2020
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“Multi-task recurrent convolutional network with correlation loss for surgical video analysis”
Yueming Jin et al · 2020
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“Glit: Neural architecture search for global and local image transformer”
Boyu Chen et al · 2021
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“Transunet: Transformers make strong encoders for medical image segmentation”
Jieneng Chen et al · 2021
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“Robotic inguinal hernia repair: systematic review and meta-analysis”
Amjad Qabbani et al · 2021
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“Large language models are few-shot clinical information extractors”
Monica Agrawal et al · 2022
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“Simvp: Simpler yet better video prediction”
Zhangyang Gao, Cheng Tan, Lirong Wu and Stan Li · 2022
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“Maskvit: Masked visual pre-training for video prediction”
Agrim Gupta et al · 2022
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“Early Experience of Pure Robotic Right Hepatectomy for Liver Donors in a Small-Volume Center”
“Can generalist foundation models outcompete special-purpose tuning? case study in medicine”
Harsha Nori et al · 2023
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“Towards expert-level medical question answering with large language models”
Karan Singhal et al · 2023
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“A foundation model for generalizable disease detection from retinal images”
Yukun Zhou et al · 2023
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Aneeq Zia et al · 2023
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“Language models are susceptible to incorrect patient self-diagnosis in medical applications”
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Eun Jang, Kwan Kim and Sung Kang · 2022
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“Whether and When does Endoscopy Domain Pretraining Make Sense?”
Dominik Batić et al · 2023
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“A visual–language foundation model for pathology image analysis using medical twitter”
Zhi Huang et al · 2023
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Chunyuan Li et al · 2023
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“EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention”
Xinyu Liu et al · 2023
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“Lovit: Long video transformer for surgical phase recognition”
Yang Liu et al · 2023
Cited alongside, same era.
Rojin Ziaei and Samuel Schmidgall · 2023
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“Segment anything in medical images”
Jun Ma et al · 2024
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“ViT-AE++: improving vision transformer autoencoder for self-supervised medical image representations”
Chinmay Prabhakar et al · 2024
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“Robots learning to imitate surgeons—challenges and possibilities”
Samuel Schmidgall, Ji Kim and Axel Krieger · 2024
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“Addressing cognitive bias in medical language models”
Samuel Schmidgall et al · 2024
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“General-purpose foundation models for increased autonomy in robot-assisted surgery”
Samuel Schmidgall et al · 2024
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