Pith. sign in

REVIEW 4 cited by

Progressive Transformer-Based Generation of Radiology Reports

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2102.09777 v3 pith:6UIZEODG submitted 2021-02-19 cs.CL

Farhad Nooralahzadeh , Nicolas Perez Gonzalez , Thomas Frauenfelder , Koji Fujimoto , Michael Krauthammer This is my paper
classification cs.CL
keywords generationradiologyimagereportsteptransformer-basedarchitecturebenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps. Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using a transformer architecture. We follow the transformer-based sequence-to-sequence paradigm at each step. We improve upon the state-of-the-art on two benchmark datasets.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation

    stat.ME 2025-07 conditional novelty 6.0 of 10

    REVTAF, a retrieval-augmented radiology report generator, reports average gains of 7.4 points on MIMIC-CXR and 2.9 points on IU X-Ray across nine metrics.

  2. Multimodal Large Language Models for Medical Report Generation via Customized Prompt Tuning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    MRG-LLM creates image-specific prompts by applying learned shifts and scales to a set of base prompts, improving automated radiology report generation on IU X-ray and MIMIC-CXR.

  3. MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MCA-RG uses concept alignment, contrastive learning, matching loss, and feature gating to generate radiology reports, reporting SOTA on MIMIC-CXR and CheXpert Plus.

  4. Automated Radiology Report Generation Based on Topic-Keyword Semantic Guidance

    cs.MM 2025-09 conditional novelty 4.0 of 10

    A topic-keyword semantic guidance framework improves automated radiology report generation and reaches state-of-the-art on two public chest X-ray benchmarks.

Pith tools