Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model GPT-4 Technical Report

Reference 1

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This paper cites Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers

Reference 2

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Gemma: Open Models Based on Gemini Research and Technology

Reference 3

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Gemma 2: Improving Open Language Models at a Practical Size

Reference 4

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Towards Generalist Biomedical AI

Reference 5

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model LLaVA-Med: Training a Large Language-and- Vision Assistant for Biomedicine in One Day

Reference 6

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This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Towards Expert-Level Medical Question Answering with Large Language Models

Reference 7

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This paper cites Geographic Distribution of CT, MRI and PET Devices in Japan: A Longitudinal Analysis Based on National Census Data.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Geographic Distribution of CT, MRI and PET Devices in Japan: A Longitudinal Analysis Based on National Census Data

Reference 8

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Observation ecf99734-0004-4004-9ccc-d16eef458234 · outbound

This paper cites Number of computed tomography scanners and regional disparities based on population and medical resources in Japan.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Number of computed tomography scanners and regional disparities based on population and medical resources in Japan

Reference 9

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This paper cites Cross-lingual Natural Language Processing on Limited Annotated Case/Radiology Reports in English and Japanese: Insights from the Real-MedNLP Workshop.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Cross-lingual Natural Language Processing on Limited Annotated Case/Radiology Reports in English and Japanese: Insights from the Real-MedNLP Workshop

Reference 10

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This paper cites Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography

Reference 11

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Observation 39328ed2-ee46-4452-beac-fb242d8cc807 · outbound

This paper cites Accessed November 16, 2024.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed November 16, 2024

Reference 12

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This paper cites JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation

Reference 13

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Measuring Massive Multitask Language Understanding

Reference 14

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Aligning AI With Shared Human Values

Reference 15

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

Reference 16

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This paper cites JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language Models.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language Models

Reference 17

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This paper cites GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

Reference 18

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This paper cites CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging

Reference 19

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model RadBERT: Adapting Transformer-based Language Models to Radiology

Reference 20

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 21

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed November 14,

Reference 22

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This paper cites Accessed November 14, 2024.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed November 14, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.946569Z

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Unresolved cited work

Reference 24

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Reference 25

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model March 11, 2024

Reference 26

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed December 12, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.867716Z

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Unresolved cited work

Reference 28

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Unsupervised Cross-lingual Representation Learning at Scale

Reference 29

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed December 12, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.849166Z

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Publicly Available Clinical BERT Embeddings

Reference 31

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Reference 32

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Preparing a collection of radiology examinations for distribution and retrieval

Reference 33

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Bleu: a Method for Automatic Evaluation of Machine Translation

Reference 34

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model ROUGE: A Package for Automatic Evaluation of Summaries

Reference 35

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

Reference 36

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Reference 37

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Reference 38

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model The Llama 3 Herd of Models

Reference 39

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model How Multilingual is Multilingual BERT? In: Korhonen A, Traum D, Màrquez L, eds

Reference 40

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.793050Z

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model End-to-End Approach for Structuring Radiology Reports

Reference 42

Resolution: verified exact; doi, observed 2026-08-11T11:03:14.073994Z

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.773066Z

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This paper cites CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Reference 44

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Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Release of Pre-Trained Models for the Japanese Language

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.753938Z

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Observation 81cbdce1-ec1a-419f-82f0-e140abaf32c9 · outbound

This paper cites Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing

Reference 46

Resolution: verified exact; doi, observed 2026-08-11T11:03:14.054540Z

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Observation accd5174-f2f1-461f-a4e7-52fe32266fbd · outbound

This paper cites BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports

Reference 47

Resolution: verified exact; doi, observed 2026-08-11T11:03:14.034638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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This paper cites December 9, 2024.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model December 9, 2024

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.679337Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ecd172bd-944f-4571-94b3-02e5a5063737 · outbound

This paper cites Accessed November 14,.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Accessed November 14,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.731466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a379f92-97f9-4f18-afb0-7a636576d8ae · outbound

This paper cites an unresolved cited work.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:03:14.715313Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation afd2bd5f-d323-4d04-9d6a-69c4866c5087 · outbound

This paper cites December 9, 2024.

Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model December 9, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:03:14.698263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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