6 citations · 7 across the 3 of their papers we have counts for
4 papers
Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation
Kaito Baba, Risa Kishikawa, Satoshi Kodera
We propose MARL-Rad, a multi-modal multi-agent reinforcement learning framework for radiology report generation that trains the entire agentic system on policy within its deployed…
JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by Evolutionary Optimization of Model Merging
Kaito Baba, Ryota Yagi, Junichiro Takahashi +2
With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas fo…
70B-parameter large language models in Japanese medical question-answering
Issey Sukeda, Risa Kishikawa, Satoshi Kodera
Since the rise of large language models (LLMs), the domain adaptation has been one of the hot topics in various domains. Many medical LLMs trained with English medical dataset have…
JMedLoRA:Medical Domain Adaptation on Japanese Large Language Models using Instruction-tuning
Issey Sukeda, Masahiro Suzuki, Hiroki Sakaji +1
In the ongoing wave of impact driven by large language models (LLMs) like ChatGPT, the adaptation of LLMs to medical domain has emerged as a crucial research frontier. Since mainst…