Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation | Paper Citation Record

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This paper cites Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736,

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This paper cites Improving patient safety: avoiding unread imaging exams in the national va en- terprise electronic health record.Journal of digital imaging, 30(3):309–313, 2017.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Improving patient safety: avoiding unread imaging exams in the national va en- terprise electronic health record.Journal of digital imaging, 30(3):309–313, 2017

Reference 2

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This paper cites Making the most of text semantics to improve biomedical vision–language processing.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Making the most of text semantics to improve biomedical vision–language processing

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Observation 043db249-8f6a-423b-a7a0-4de7426c0ad3 · outbound

This paper cites Generating radiology reports via memory-driven transformer.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Generating radiology reports via memory-driven transformer

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This paper cites Generating radiology reports via memory-driven transformer.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Generating radiology reports via memory-driven transformer

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Observation 4a98aefb-75fd-4731-88dd-e787b4f6ff02 · outbound

This paper cites Cross-modal memory networks for radiology report genera- tion.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Cross-modal memory networks for radiology report genera- tion

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This paper cites Cross-modal memory networks for radiology report genera- tion.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Cross-modal memory networks for radiology report genera- tion

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This paper cites Is the modality gap a bug or a feature? a robustness perspective.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Is the modality gap a bug or a feature? a robustness perspective

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Observation a01b08ab-e0c0-4ceb-9258-3337f3c2b168 · outbound

This paper cites Meshed-memory transformer for image caption- ing.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Meshed-memory transformer for image caption- ing

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Observation 024a0d4e-b347-4064-8db1-97f2d95b481c · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013

Reference 10

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Observation c35bc65b-61a3-457f-860e-ce55829eb2fc · outbound

This paper cites Preparing a collection of radiology examinations for distribution and retrieval.Journal of the American Medical Informatics As- sociation, 23(2):304–310, 2015.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Preparing a collection of radiology examinations for distribution and retrieval.Journal of the American Medical Informatics As- sociation, 23(2):304–310, 2015

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Observation a8bd02ae-c747-4f2c-803e-0ba7d0d194e9 · outbound

This paper cites Huang, X.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Huang, X

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Observation d559602f-7c9d-43f2-834e-0c0d871a4087 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

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Observation 9b9f9c3a-0e7a-479f-8b6f-f5680deefa73 · outbound

This paper cites RadGraph: Extracting Clinical Entities and Relations from Radiology Reports.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

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Observation 8074cc65-5078-4e1d-adcc-ff4ae96a6374 · outbound

This paper cites On the automatic generation of medical imaging reports.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation On the automatic generation of medical imaging reports

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This paper cites Show, describe and conclude: On exploiting the structure information of chest x-ray reports.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Show, describe and conclude: On exploiting the structure information of chest x-ray reports

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This paper cites MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

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This paper cites an unresolved cited work.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Unresolved cited work

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This paper cites Llava-med: Training a large language- and-vision assistant for biomedicine in one day.Advances in Neural Information Processing Systems, 36:28541–28564,.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Llava-med: Training a large language- and-vision assistant for biomedicine in one day.Advances in Neural Information Processing Systems, 36:28541–28564,

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Observation 951829f1-b181-4743-9f94-2c86a4aa449e · outbound

This paper cites Dynamic graph enhanced contrastive learning for chest X-ray report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Dynamic graph enhanced contrastive learning for chest X-ray report generation

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This paper cites Con- trastive learning with counterfactual explanations for radiol- ogy report generation, 2024.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Con- trastive learning with counterfactual explanations for radiol- ogy report generation, 2024

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Unresolved cited work

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This paper cites Bootstrapping large language models for radiology report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Bootstrapping large language models for radiology report generation

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Observation e01706d4-6e81-4aac-a34d-2df1081a330c · outbound

This paper cites Competence-based mul- timodal curriculum learning for medical report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Competence-based mul- timodal curriculum learning for medical report generation

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Observation 2f355c2e-178d-4cc6-b9a2-0e7766ac1511 · outbound

This paper cites Exploring and distilling posterior and prior knowledge for 9 radiology report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Exploring and distilling posterior and prior knowledge for 9 radiology report generation

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Observation faffe228-4ef2-42f0-915e-2288f8654241 · outbound

This paper cites Contrastive attention for automatic chest x-ray report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Contrastive attention for automatic chest x-ray report generation

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Struc- tural entities extraction and patient indications incorporation for chest x-ray report generation

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This paper cites Enhanced contrastive learning with multi-view longitudinal data for chest x-ray report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Enhanced contrastive learning with multi-view longitudinal data for chest x-ray report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation In-context learning for zero-shot medical re- port generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation In-context learning for zero-shot medical re- port generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation In-context learning for zero-shot medical re- port generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation From observation to concept: A flexible multi-view paradigm for medical report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Improving factual completeness and con- sistency of image-to-text radiology report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Improving Chest X-Ray Report Generation by Leveraging Warm Starting

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Lon- gitudinal data and a semantic similarity reward for chest x-ray report generation, 2023

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Dart: Disease-aware image-text alignment and self-correcting re-alignment for trustworthy radiology report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Rad-dino: Exploring scalable medical image encoders beyond text supervision; 2024

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Now Founda- tions and Trends, 2019

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This paper cites Im- proving radiology report generation systems by removing hallucinated references to non-existent priors.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Im- proving radiology report generation systems by removing hallucinated references to non-existent priors

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Rosenow III

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Radiologist shortage leaves patient care at risk, warns royal college.BMJ, 359, 2017

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Observation 834ee2e4-63c1-459a-94eb-4a64a80dbc00 · outbound

This paper cites Rosenkrantz, Danny R.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Rosenkrantz, Danny R

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Automatic radiology reports generation via memory align- ment network

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Automatic radiology reports generation via memory align- ment network

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Cwcd: Category-wise contrastive decoding for structured medical report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Interactive and explainable region-guided radi- ology report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Gomez, Lukasz Kaiser, and Illia Polosukhin

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Show and tell: A neural image caption generator

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Multi-granularity cross-modal align- ment for generalized medical visual representation learning

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Hergen: Ele- vating radiology report generation with longitudinal data

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Unresolved cited work

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation A medical semantic-assisted transformer for ra- diographic report generation

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Metransformer: Radiology report generation by transformer with multiple learnable expert tokens

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Observation 4435eeae-33f2-43de-a9f8-7edd7d06ffbc · outbound

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Medklip: Medical knowledge enhanced language- image pre-training for x-ray diagnosis

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Observation 65bd577b-b453-4438-90ef-72adbf5ba585 · outbound

This paper cites Chest ImaGenome Dataset for Clinical Reasoning.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Chest ImaGenome Dataset for Clinical Reasoning

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Source-reported events for the cited work

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Observation 59050111-4ca8-4738-9025-68d8cdfdba26 · outbound

This paper cites Show, attend and tell: Neural image caption genera- tion with visual attention.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Show, attend and tell: Neural image caption genera- tion with visual attention

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Observation a76eac96-b732-464a-a3a5-fd04f9935895 · outbound

This paper cites Multimodal recurrent model with attention for automated radiology report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Multimodal recurrent model with attention for automated radiology report generation

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Resolution: unresolved; no resolver link, observed 2026-08-04T00:54:37.326552Z

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Observation 473a421c-51c0-4694-bed0-e06e1e048249 · outbound

This paper cites Kuo, Subathra Adithan, Eduardo Pontes Reis, Stephen Kwak, Vasantha Ku- mar Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, and Michael Moor.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Kuo, Subathra Adithan, Eduardo Pontes Reis, Stephen Kwak, Vasantha Ku- mar Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, and Michael Moor

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Resolution: unresolved; no resolver link, observed 2026-08-04T00:54:37.386714Z

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Observation 3031411c-3844-46b5-b3ea-8a3f34bebbba · outbound

This paper cites Automatic gener- ation of medical imaging diagnostic report with hierarchical recurrent neural network.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Automatic gener- ation of medical imaging diagnostic report with hierarchical recurrent neural network

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Observation 6bcb8dc1-f01c-416d-9c57-8b7b79934011 · outbound

This paper cites Aligntransformer: Hierarchical alignment of visual regions and disease tags for medical report generation.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Aligntransformer: Hierarchical alignment of visual regions and disease tags for medical report generation

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Observation 5a9cf5ba-2d2e-4d6c-a77f-4d16d0caf39b · outbound

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Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Image captioning with semantic attention

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Observation 6a1f106f-df44-4f92-8ed3-a3e0c67a01fd · outbound

This paper cites A clinically accessible small multimodal radiology model and evaluation metric for chest x-ray findings.Nature Communications, 16 (1):3108, 2025.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation A clinically accessible small multimodal radiology model and evaluation metric for chest x-ray findings.Nature Communications, 16 (1):3108, 2025

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Observation c8348acf-1580-49a8-8946-17dca487d8e2 · outbound

This paper cites RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-04T00:54:37.672577Z

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Observation 792391cd-7d53-431c-96d2-48ab87e679e9 · outbound

This paper cites Knowledge-enhanced visual-language pre- training on chest radiology images.Nature Communications, 14(1):4542, 2023.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation Knowledge-enhanced visual-language pre- training on chest radiology images.Nature Communications, 14(1):4542, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-04T00:54:37.742982Z

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Observation 797865b6-c265-41e4-8227-f607025f167e · outbound

This paper cites When radiology report generation meets knowledge graph.

Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation When radiology report generation meets knowledge graph

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Resolution: unresolved; no resolver link, observed 2026-08-04T00:54:37.900938Z

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