3 papers
cs.CL2025
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation
Hisashi Johno, Yuki Johno, Akitomo Amakawa +9
Purpose: Retrieval-augmented generation (RAG) is a technology to enhance the functionality and reliability of large language models (LLMs) by retrieving relevant information from r…
cs.CL2024
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging
Ryota Tozuka, Hisashi Johno, Akitomo Amakawa +5
Purpose: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged…
math.ST2024
Extension of the one-sample Kolmogorov-Smirnov test
Atsushi Komaba, Hisashi Johno, Kazunori Nakamoto
We propose here a new goodness-of-fit test, named the one-sample OVL-q test (q = 1, 2, . . .), which can be considered an extension of the one-sample Kolmogorov-Smirnov test (equiv…