Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Usuyama, Naoto"'
Autor:
Zhao, Theodore, Gu, Yu, Yang, Jianwei, Usuyama, Naoto, Lee, Ho Hin, Naumann, Tristan, Gao, Jianfeng, Crabtree, Angela, Abel, Jacob, Moung-Wen, Christine, Piening, Brian, Bifulco, Carlo, Wei, Mu, Poon, Hoifung, Wang, Sheng
Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. Holistic image analysis comprises interdependent subtasks such as segmentation, detection, and recognition of
Externí odkaz:
http://arxiv.org/abs/2405.12971
Autor:
Chaves, Juan Manuel Zambrano, Huang, Shih-Cheng, Xu, Yanbo, Xu, Hanwen, Usuyama, Naoto, Zhang, Sheng, Wang, Fei, Xie, Yujia, Khademi, Mahmoud, Yang, Ziyi, Awadalla, Hany, Gong, Julia, Hu, Houdong, Yang, Jianwei, Li, Chunyuan, Gao, Jianfeng, Gu, Yu, Wong, Cliff, Wei, Mu, Naumann, Tristan, Chen, Muhao, Lungren, Matthew P., Chaudhari, Akshay, Yeung-Levy, Serena, Langlotz, Curtis P., Wang, Sheng, Poon, Hoifung
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising results on some biomedical benchmarks, there are still major challenges
Externí odkaz:
http://arxiv.org/abs/2403.08002
This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner. Specifically, we split the gigapixel image into a sequence of millions of patches and project them linearly into embeddings. LongN
Externí odkaz:
http://arxiv.org/abs/2312.03558
Autor:
Liu, Qianchu, Hyland, Stephanie, Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Wetscherek, Maria Teodora, Tinn, Robert, Sharma, Harshita, Pérez-García, Fernando, Schwaighofer, Anton, Rajpurkar, Pranav, Khanna, Sameer Tajdin, Poon, Hoifung, Usuyama, Naoto, Thieme, Anja, Nori, Aditya V., Lungren, Matthew P., Oktay, Ozan, Alvarez-Valle, Javier
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performa
Externí odkaz:
http://arxiv.org/abs/2310.14573
Autor:
Gu, Yu, Yang, Jianwei, Usuyama, Naoto, Li, Chunyuan, Zhang, Sheng, Lungren, Matthew P., Gao, Jianfeng, Poon, Hoifung
Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be applied to counterfactual image generation, which helps differentiate cau
Externí odkaz:
http://arxiv.org/abs/2310.10765
Autor:
Wong, Cliff, Zhang, Sheng, Gu, Yu, Moung, Christine, Abel, Jacob, Usuyama, Naoto, Weerasinghe, Roshanthi, Piening, Brian, Naumann, Tristan, Bifulco, Carlo, Poon, Hoifung
Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching
Externí odkaz:
http://arxiv.org/abs/2308.02180
Autor:
Gu, Yu, Zhang, Sheng, Usuyama, Naoto, Woldesenbet, Yonas, Wong, Cliff, Sanapathi, Praneeth, Wei, Mu, Valluri, Naveen, Strandberg, Erika, Naumann, Tristan, Poon, Hoifung
Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that whil
Externí odkaz:
http://arxiv.org/abs/2307.06439
Autor:
Li, Chunyuan, Wong, Cliff, Zhang, Sheng, Usuyama, Naoto, Liu, Haotian, Yang, Jianwei, Naumann, Tristan, Poon, Hoifung, Gao, Jianfeng
Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversational AI has seen rapid progress by leveraging billions of image-text pai
Externí odkaz:
http://arxiv.org/abs/2306.00890
Autor:
Liu, Fangyu, Liu, Qianchu, Bannur, Shruthi, Pérez-García, Fernando, Usuyama, Naoto, Zhang, Sheng, Naumann, Tristan, Nori, Aditya, Poon, Hoifung, Alvarez-Valle, Javier, Oktay, Ozan, Hyland, Stephanie L.
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain l
Externí odkaz:
http://arxiv.org/abs/2303.13386
Autor:
Zhang, Sheng, Xu, Yanbo, Usuyama, Naoto, Xu, Hanwen, Bagga, Jaspreet, Tinn, Robert, Preston, Sam, Rao, Rajesh, Wei, Mu, Valluri, Naveen, Wong, Cliff, Tupini, Andrea, Wang, Yu, Mazzola, Matt, Shukla, Swadheen, Liden, Lars, Gao, Jianfeng, Lungren, Matthew P., Naumann, Tristan, Wang, Sheng, Poon, Hoifung
Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process different modalities of data, including text and images. Therefore, training
Externí odkaz:
http://arxiv.org/abs/2303.00915