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arxiv: 2605.31093 · v1 · pith:BDZ2H4BVnew · submitted 2026-05-29 · 💻 cs.CV

Cross-Modal Clinical Knowledge Integration for Mammography Report Generation

Jiayi Zhu , Fuxiang Huang , Yu Xie , Xi Wang , Zhixuan Chen , Yuan Guo , Qingcong Kong , Zhenhui Li
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Qiong Luo Hao Chen
This is my paper

Pith reviewed 2026-06-28 23:08 UTC · model grok-4.3

classification 💻 cs.CV
keywords mammography report generationBI-RADSclinical knowledge integrationmulti-view mammogramstwo-stage trainingterminology-aware fine-tuningreport parsing tool
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The pith

MammoRG generates mammography reports by simulating BI-RADS clinical reasoning in a two-stage process that first classifies multi-view images and then fine-tunes on terminology.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that direct image-to-text methods miss the structured reasoning radiologists follow under BI-RADS guidelines when writing mammography reports. MammoRG addresses this with a two-stage framework: the first stage uses classification supervision to integrate prior knowledge from a patient's four-view mammograms, and the second stage applies terminology-aware fine-tuning to treat clinical terms as atomic units. This produces reports that score higher on clinical metrics, especially diagnosis-related BI-RADS F1, across internal and external datasets. The authors also introduce MammoRGTool to extract structured information from free-text reports for evaluation. If correct, the approach shows that explicit clinical workflow modeling improves consistency in automated reporting for breast cancer screening.

Core claim

MammoRG adopts a two-stage training framework. In the first stage, the model learns to integrate clinically relevant prior knowledge from a patient's four-view mammograms through classification-based supervision. In the second stage, a terminology-aware supervised fine-tuning strategy is introduced to model mammography-specific clinical terms as atomic semantic units, enabling the generation of high-quality reports with improved clinical consistency.

What carries the argument

The two-stage training framework that follows the BI-RADS guideline by combining classification-based multi-view knowledge integration with terminology-aware supervised fine-tuning.

If this is right

  • MammoRG produces higher BI-RADS F1 scores than prior methods, with gains of 2.73%, 2.04%, 1.90%, and 3.27% on the internal, external 1, external 2, and VinDr-Mammo datasets.
  • Generated reports exhibit improved clinical consistency through explicit modeling of mammography-specific terms.
  • MammoRGTool enables automated extraction of structured clinical information from free-text reports for quantitative evaluation.
  • The framework reduces reliance on direct visual-to-text mapping by incorporating prior clinical knowledge from multiple views.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The classification supervision step may reduce factual errors in reports by grounding generation in explicit diagnostic categories before text production.
  • Similar two-stage pipelines could be tested on other structured reporting tasks such as chest X-ray or pathology report generation.
  • Performance on external datasets suggests the method may transfer across different imaging equipment and patient populations, though this would need separate confirmation.

Load-bearing premise

The two-stage training framework with classification-based supervision followed by terminology-aware supervised fine-tuning actually captures and simulates the structured clinical reasoning process followed by radiologists under the BI-RADS guideline.

What would settle it

A controlled test in which the BI-RADS F1 gains disappear when the second-stage terminology modeling is removed or when the model is evaluated on reports that do not follow BI-RADS structure.

Figures

Figures reproduced from arXiv: 2605.31093 by Fuxiang Huang, Hao Chen, Jiayi Zhu, Qingcong Kong, Qiong Luo, Xi Wang, Yuan Guo, Yu Xie, Zhenhui Li, Zhixuan Chen.

Figure 1
Figure 1. Figure 1: Examples demonstrating limitations of general models in mammography reporting and the comparison between traditional uni-modal methods and our cross-modal method. However, despite the rapid advancement of report generation technology, existing general-purpose radiology report generation models cannot be directly adapted to mammography. Most existing methods are primarily designed for generic single-image v… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of MammoRG. This figure illustrates the two-stage training process and the inference workflow, where 𝐿, 𝑀, and 𝑆 in stage 1 represent Located_at, Modified_by, and Suggestive_of, respectively. and fed into a text decoder (𝐷txt) to generate the final mammography report: 𝑌̂ = 𝐷txt(𝐯, 𝐤), (2) where 𝐤 denotes the integrated clinical knowledge features. Training Strategy. The training procedure consists… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of MammoRGTool. This figure illustrates the two phases of MammoRGTool development and compares the performance of Qwen3-32B, MammoRGTool, and MammoRGTool with post-processing on 50 manually annotated samples. we initialize each new token embedding using the mean of existing embeddings with small random perturbations, providing a stable starting point that is consistent with the original embedding … view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative examples of the generated report of the baseline and the proposed method. Blue font indicates consistent content with the ground-truth, while red font indicates incorrect content. whereas view-level features contribute more to fine-grained relational modeling. c) The combination of patient-level and view-level features demonstrates high efficiency with minimal token cost. Using only 5 tokens, t… view at source ↗
Figure 5
Figure 5. Figure 5: Error analysis of MammoRG predictions. (a) Confusion matrix of BI-RADS classification, showing the distribution of predicted categories against ground-truth labels. (b) Error rates for each BI-RADS category, defined as the proportion of incorrect predictions within each class. (c) Confusion matrix of breast composition classification. (d) Error rates for each composition category. (e-f) Finding-level analy… view at source ↗
read the original abstract

Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examinations creates a substantial workload for radiologists, making accurate and consistent report generation a critical clinical challenge. Existing automated mammography report generation methods primarily focus on direct visual-to-text mapping, while overlooking the structured clinical reasoning process followed by radiologists in real-world practice. To address this limitation, we propose MammoRG, a mammography report generation framework that explicitly simulates the clinical reporting workflow by following the BI-RADS guideline and incorporating prior clinical knowledge to produce diagnostic reports. Specifically, MammoRG adopts a two-stage training framework. In the first stage, the model learns to integrate clinically relevant prior knowledge from a patient's four-view mammograms through classification-based supervision. In the second stage, a terminology-aware supervised fine-tuning strategy is introduced to model mammography-specific clinical terms as atomic semantic units, enabling the generation of high-quality reports with improved clinical consistency. To facilitate clinical efficacy evaluation of generated reports, we further develop MammoRGTool, a dedicated mammography report parsing tool that extracts structured clinical information from free-text reports. Extensive experiments demonstrate that MammoRG consistently outperforms existing methods across multiple clinical efficacy metrics, particularly in diagnosis-related BI-RADS F1, where it surpasses the second-best model by 2.73%, 2.04%, 1.90%, and 3.27% on the internal, external 1, external 2, and VinDr-Mammo datasets, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes MammoRG, a two-stage mammography report generation framework that simulates BI-RADS-guided clinical reasoning: stage 1 uses classification-based supervision to integrate prior knowledge from four-view mammograms, and stage 2 applies terminology-aware supervised fine-tuning to treat clinical terms as atomic units. It introduces MammoRGTool to parse free-text reports for structured clinical metrics and claims consistent outperformance over baselines, with BI-RADS F1 gains of 2.73%, 2.04%, 1.90%, and 3.27% on the internal, external 1, external 2, and VinDr-Mammo datasets.

Significance. If the empirical gains hold under validated evaluation, the work could advance clinically aligned report generation by explicitly modeling structured diagnostic reasoning rather than direct visual-to-text mapping, with the dedicated parsing tool offering a step toward more meaningful efficacy assessment in medical imaging.

major comments (3)
  1. [MammoRGTool description] MammoRGTool section: The tool is presented as extracting structured clinical information for BI-RADS F1 computation, yet no validation metrics (precision/recall/F1 against radiologist-annotated free-text reports on a held-out set) are reported. This is load-bearing for the central claim, as unvalidated parsing errors could artifactually inflate the reported 2-3% margins if they correlate with model outputs.
  2. [Experiments] Experiments section: No statistical significance tests, confidence intervals, or p-values are provided for the BI-RADS F1 and other metric differences, and baseline implementation details (e.g., exact architectures, training hyperparameters) are insufficiently specified to rule out confounding factors in the cross-dataset comparisons.
  3. [§3] §3 (two-stage framework): The claim that classification-based supervision followed by terminology-aware fine-tuning simulates radiologists' BI-RADS reasoning process lacks supporting ablations (e.g., vs. standard end-to-end fine-tuning) or qualitative analysis showing alignment with guideline-structured outputs, weakening attribution of gains to the proposed clinical integration.
minor comments (1)
  1. [Abstract] Abstract: Dataset sizes, BI-RADS category distributions, and exact number of views per case could be stated explicitly to aid quick assessment of experimental scope.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation and evidence.

read point-by-point responses

  1. Referee: [MammoRGTool description] MammoRGTool section: The tool is presented as extracting structured clinical information for BI-RADS F1 computation, yet no validation metrics (precision/recall/F1 against radiologist-annotated free-text reports on a held-out set) are reported. This is load-bearing for the central claim, as unvalidated parsing errors could artifactually inflate the reported 2-3% margins if they correlate with model outputs.

    Authors: We agree that validation metrics for MammoRGTool are necessary to substantiate the BI-RADS F1 results. The current manuscript does not report precision, recall, or F1 against radiologist annotations on a held-out set. In the revised version, we will add these metrics from a dedicated validation study. revision: yes

  2. Referee: [Experiments] Experiments section: No statistical significance tests, confidence intervals, or p-values are provided for the BI-RADS F1 and other metric differences, and baseline implementation details (e.g., exact architectures, training hyperparameters) are insufficiently specified to rule out confounding factors in the cross-dataset comparisons.

    Authors: We acknowledge that statistical tests and fuller baseline details are missing. We will incorporate p-values, confidence intervals for all reported differences, and expanded specifications of baseline models and hyperparameters in the revised experiments section. revision: yes

  3. Referee: [§3] §3 (two-stage framework): The claim that classification-based supervision followed by terminology-aware fine-tuning simulates radiologists' BI-RADS reasoning process lacks supporting ablations (e.g., vs. standard end-to-end fine-tuning) or qualitative analysis showing alignment with guideline-structured outputs, weakening attribution of gains to the proposed clinical integration.

    Authors: While the two-stage design follows BI-RADS clinical workflow, we agree that ablations and qualitative evidence would better support attribution of gains. The revised manuscript will include comparisons against end-to-end fine-tuning baselines and qualitative report examples aligned with guideline structure. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on held-out dataset comparisons

full rationale

The paper's derivation consists of a two-stage training procedure (classification supervision then terminology-aware fine-tuning) followed by empirical evaluation on internal/external/VinDr-Mammo datasets using BI-RADS F1 and other metrics extracted via the authors' MammoRGTool. No equation, prediction, or central result is shown to reduce by construction to a fitted parameter or self-referential definition; the reported gains are presented as direct comparisons against baselines on held-out data. Any self-citations (if present) are not load-bearing for the performance claims, and the tool is introduced solely for evaluation rather than as part of a self-defining loop. This is the standard non-circular empirical pattern.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the effectiveness of the two-stage training process and the assumption that BI-RADS provides a usable structured clinical workflow; model parameters are fitted to data during training.

free parameters (1)
  • neural network parameters
    Weights of the vision-language model are fitted during the classification stage and the terminology-aware fine-tuning stage on mammography datasets.
axioms (1)
  • domain assumption BI-RADS guideline defines a structured clinical reasoning process that can be simulated by classification supervision and terminology-aware fine-tuning
    Invoked in the description of the two-stage framework that explicitly follows the BI-RADS guideline.
invented entities (1)
  • MammoRGTool no independent evidence
    purpose: Extracts structured clinical information such as BI-RADS categories from free-text generated reports for evaluation
    New parsing tool developed to enable clinical efficacy metrics; no independent evidence provided outside this work.

pith-pipeline@v0.9.1-grok · 5830 in / 1362 out tokens · 35990 ms · 2026-06-28T23:08:09.076027+00:00 · methodology

discussion (0)

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Reference graph

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