SURE-Med: Reliable Chest X-Ray Report Generation

• SURE-Med is a framework for chest X-ray report generation that explicitly reduces visual, label distribution, and contextual uncertainties.
• It integrates specialized modules—FAVR for accurate view repair, TSL for tailoring language to rare pathologies, and CEF for filtering outdated context.
• The approach achieves state-of-the-art performance on MIMIC-CXR and IU-Xray, improving recall for rare conditions and generalization across institutions.

Searching arXiv for the specified SURE-Med paper and closely related work to ground the article. SURE-Med is a chest X-ray medical report generation framework that targets reliability by explicitly reducing three sources of uncertainty: visual uncertainty from noisy or incorrect view annotations, label distribution uncertainty from long-tailed disease prevalence, and contextual uncertainty from unverified historical reports. It is formulated for radiology report generation from current multi-view chest radiographs and optional prior reports, and combines Frontal-Aware View-Repair Resampling, Token-Sensitive Learning, and a Contextual Evidence Filter within a unified architecture built on XraySigLIP and Vicuna-7B v1.5 with LoRA. On MIMIC-CXR and IU-Xray, it is reported to achieve state-of-the-art performance while improving clinical reliability and cross-dataset generalization (Gu et al., 3 Aug 2025).

1. Conceptual scope and uncertainty formulation

Radiology report generation, or medical report generation, seeks to produce clinically meaningful free-text reports from medical images. In the SURE-Med formulation, the task is chest X-ray report generation centered on the current visit’s “Findings” section, using one or more current radiographs and, when available, up to two prior reports. The framework analyzes unreliability through three uncertainty types: visual uncertainty, label distribution uncertainty, and contextual uncertainty (Gu et al., 3 Aug 2025).

Visual uncertainty arises because chest X-rays are acquired in frontal and lateral or oblique views, while view metadata in large datasets are often wrong, missing, or marked as unknown or special. This can cause a model to treat a lateral image as frontal or to fuse multi-view evidence without a clinically meaningful hierarchy. Label distribution uncertainty arises from long-tailed pathology prevalence: common findings dominate the training distribution, while many clinically important abnormalities appear in less than $1\%$ of cases. Contextual uncertainty arises when prior reports are incorporated naively; historical text can contain outdated findings, hallucinations, or content describing a different clinical state.

Within this formulation, SURE-Med is not merely a generic multimodal encoder-decoder. Its explicit claim is that these three uncertainties should be reduced by dedicated mechanisms rather than left to be absorbed implicitly by sequence-to-sequence training. This design choice distinguishes it from approaches that simply add more views or more historical context without controlling the noise those sources introduce.

2. Frontal-Aware View-Repair Resampling

The module for visual uncertainty reduction is Frontal-Aware View-Repair Resampling, abbreviated FAVR. Its first function is view repair: a pretrained view classifier relabels each current image as frontal or lateral, resolving incorrect, ambiguous, or missing metadata. For a current visit $X_i^{\mathrm{cur}}$, the images are partitioned into frontal images $X_i^f$ and lateral images $X_i^\ell$ (Gu et al., 3 Aug 2025).

All current images are then processed by a shared frozen image encoder $E_v$, specifically XraySigLIP, to produce tokenized visual features: $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$

$$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$

FAVR introduces learnable disease query tokens $V' \in \mathbb{R}^{N_q \times D}$, with $N_q = 128$. These queries first attend to the frontal features through a frontal resampler: $T^f = r_f(H^f, V') \in \mathbb{R}^{N_q \times D}.$ The resulting frontal tokens are then used as queries for lateral feature selection: $X_i^{\mathrm{cur}}$0 Finally, the frontal and lateral tokens are concatenated and projected: $X_i^{\mathrm{cur}}$1

This architecture encodes a specific clinical prior: the frontal view is primary, and lateral views are supplementary. Rather than treating all views symmetrically, FAVR makes lateral feature selection conditional on what the frontal pathway already considers diagnostically salient. The intended effect is not only better feature fusion, but reduction of the ambiguity introduced by incorrect view labels and underconstrained multi-view aggregation.

3. Token-Sensitive Learning and long-tailed diagnostic language

The module for label distribution uncertainty is Token-Sensitive Learning, or TSL. Its premise is that class imbalance in radiology does not act only at the image-label level; it also affects the language generation channel, because sequence losses place most gradient mass on common diagnostic patterns. SURE-Med therefore weights sentence-level supervision according to disease rarity (Gu et al., 3 Aug 2025).

The current report $X_i^{\mathrm{cur}}$2 is split into sentences $X_i^{\mathrm{cur}}$3. Each sentence is passed through CheXbert, yielding a 14-label vector, and the set of disease labels associated with sentence $X_i^{\mathrm{cur}}$4 is

$X_i^{\mathrm{cur}}$5

Label 14, “No Finding,” is ignored. Sentences with at least one positive or uncertain disease label are treated as key diagnostic sentences.

For each label $X_i^{\mathrm{cur}}$6, SURE-Med computes its frequency $X_i^{\mathrm{cur}}$7 in the MIMIC-CXR training set. The sentence weight depends on the least frequent label it contains: $X_i^{\mathrm{cur}}$8 with $X_i^{\mathrm{cur}}$9 and $X_i^f$0. These raw weights are normalized into $X_i^f$1, with $X_i^f$2: $X_i^f$3

The training loss combines standard token-level cross-entropy with a weighted key-sentence term: $X_i^f$4

$X_i^f$5

$X_i^f$6

The implementation uses an imbalance loss weight $X_i^f$7, which plays the role of $X_i^f$8.

The reported empirical effect is selective. Recall increases substantially, and per-class F1 for rare conditions such as “Pleural Other” and “Consolidation” more than doubles. For frequent classes such as Cardiomegaly, Support Devices, and Lung Opacity, gains are modest or slightly negative. This suggests that TSL reallocates training emphasis toward diagnostically important tail phenomena rather than uniformly improving all report tokens.

4. Contextual Evidence Filter and end-to-end integration

The module for contextual uncertainty reduction is the Contextual Evidence Filter, or CEF. Its purpose is to exploit longitudinal information without allowing outdated or irrelevant findings to propagate into the current report. Prior reports $X_i^f$9 and $X_i^\ell$0 are split into sentences

$X_i^\ell$1

Each prior sentence is first filtered with CheXbert, retaining only sentences with at least one positive disease label among the first 13 categories. This removes boilerplate and non-disease text (Gu et al., 3 Aug 2025).

The remaining sentences are encoded with a CLIP text encoder, while the current images are encoded with a CLIP image encoder into a pooled visual embedding $X_i^\ell$2. For each retained sentence embedding $X_i^\ell$3, image-text agreement is measured by cosine similarity: $X_i^\ell$4 A fixed threshold can be used, but SURE-Med introduces dynamic thresholding. The base threshold is approximately $X_i^\ell$5, and a higher threshold is applied to “vanished findings,” defined as conditions present in an older report but absent in the most recent prior report. For such sentences, the similarity requirement is stricter: $X_i^\ell$6 with $X_i^\ell$7 for vanished findings.

The filtered historical sentences are then used as text context for the decoder. The end-to-end backbone couples a frozen XraySigLIP vision encoder with a Vicuna-7B v1.5 language decoder fine-tuned through LoRA. The decoder receives the fused visual tokens $X_i^\ell$8 from FAVR and the filtered prior text from CEF, and training is driven by $X_i^\ell$9. The paper states that there is no separate explicit loss term per module; FAVR, CEF, and TSL are optimized jointly through the final generation objective.

Training uses a two-stage curriculum. In Stage 1, XraySigLIP and Vicuna are frozen, lateral images and prior reports are removed, and only the frontal resampler and projection are trained for 2 epochs on frontal images. In Stage 2, XraySigLIP remains frozen, LoRA adapters are enabled in Vicuna, lateral images and prior reports are introduced, and FAVR, CEF, and TSL are trained jointly for 4 epochs. This staged procedure is designed to stabilize frontal representations before multi-view and longitudinal integration.

5. Benchmarks, ablations, and empirical profile

SURE-Med is evaluated on MIMIC-CXR and on IU-Xray as a cross-dataset generalization test. On MIMIC-CXR, the framework is reported as state of the art in several natural-language and clinical metrics: BLEU-1 $E_v$0, BLEU-4 $E_v$1, ROUGE-L $E_v$2, METEOR $E_v$3, precision $E_v$4, recall $E_v$5, and F1 $E_v$6. Precision is second best, while recall and F1 are best; the paper emphasizes the trade of a small precision decrease for a larger recall increase relative to the prior best system (Gu et al., 3 Aug 2025).

Benchmark	Reported metrics	Interpretation
MIMIC-CXR	BLEU-1 0.432, BLEU-4 0.170, ROUGE-L 0.328, METEOR 0.196, P 0.527, R 0.516, F1 0.521	State-of-the-art overall; best recall and F1
IU-Xray	P 0.456, R 0.484, F1 0.431	Cross-dataset generalization without fine-tuning

On IU-Xray, SURE-Med is trained only on MIMIC-CXR and then directly tested, yielding precision $E_v$7, recall $E_v$8, and F1 $E_v$9. The paper characterizes this as a huge margin over the compared baseline and interprets it as evidence that the uncertainty-reduction mechanisms improve generalization across institutions and reporting styles.

Ablation studies isolate the contribution of each module. Starting from a baseline with none of FAVR, CEF, or TSL, the reported scores are BLEU-1 $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$0, BLEU-4 $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$1, ROUGE-L $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$2, METEOR $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$3, precision $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$4, recall $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$5, and F1 $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$6. Adding FAVR alone raises F1 to $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$7, with precision $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$8 and recall $H_i^f = E_v(X_i^f) \in \mathbb{R}^{N_i \times D},$9. Adding FAVR and TSL yields precision $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$0, recall $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$1, and F1 $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$2, indicating that TSL chiefly improves recall. Adding FAVR and CEF yields precision $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$3, recall $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$4, and F1 $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$5, indicating that CEF chiefly improves precision. The full configuration, FAVR + CEF + TSL, reaches precision $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$6, recall $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$7, and F1 $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$8, which the paper presents as the best balance.

The history-filtering ablation further separates thresholding effects within CEF. Without filtering, precision is $$H_i^\ell = E_v(X_i^\ell) \in \mathbb{R}^{N_i \times D}.$$9, recall $V' \in \mathbb{R}^{N_q \times D}$0, and F1 $V' \in \mathbb{R}^{N_q \times D}$1. A fixed threshold improves these to $V' \in \mathbb{R}^{N_q \times D}$2, $V' \in \mathbb{R}^{N_q \times D}$3, and $V' \in \mathbb{R}^{N_q \times D}$4. Dynamic thresholding reaches precision $V' \in \mathbb{R}^{N_q \times D}$5, recall $V' \in \mathbb{R}^{N_q \times D}$6, and F1 $V' \in \mathbb{R}^{N_q \times D}$7, giving the highest precision and F1.

Qualitative examples reinforce these quantitative claims. In one case, FAVR relabels an “UNK” frontal view as AP and aligns it with the lateral image, while the baseline misses mild cardiomegaly and important lung findings. In another, CEF discards outdated fracture history, retains support-device information, and suppresses hallucinated mention of a resolved hydropneumothorax. The paper attributes improved rare-condition phrasing in such cases to TSL.

6. Interpretation, limitations, and nomenclature

A common misunderstanding would be to treat SURE-Med as an explicit uncertainty estimator. The paper states the opposite: it focuses on reducing uncertainty rather than exposing uncertainty estimates or confidence scores. Its outputs are more reliable reports, not calibrated uncertainty maps or token-level confidence intervals. Future work is explicitly suggested to integrate uncertainty estimation and interactive reporting (Gu et al., 3 Aug 2025).

Several limitations are identified. View repair depends on a pretrained view classifier and a curated view-repair dataset; substantial shifts in acquisition protocols may require retraining. CEF depends on CheXbert and CLIP similarity, both largely aligned to MIMIC-like distributions. The framework is developed for chest X-rays; extending it to CT, MRI, ultrasound, or pathology would require different view taxonomies, label sets, and longitudinal assumptions. The model stack—Vicuna-7B, XraySigLIP, CLIP, and CheXbert—is comparatively heavy, even though the visual encoder is frozen and LoRA is used for efficiency.

The framework also defines a particular stance on clinical deployment. It is presented as a decision-support tool rather than a replacement for radiologists, and the paper emphasizes the continuing need for validation and human oversight. This positioning is consistent with broader work arguing that medical vision-language systems require systematic robustness analysis rather than purely accuracy-based evaluation (Kahl et al., 2024).

The name itself is potentially ambiguous in the recent literature. “SURE-Med” has also been used for an evidential segmentation instantiation of the SURE framework in medical image segmentation, where the focus is self-supervised uncertainty supervision over boundaries and noise rather than report generation (Li et al., 21 Sep 2025). In the report-generation context, however, SURE-Med specifically denotes “Systematic Uncertainty Reduction for Enhanced Reliability in Medical Report Generation,” centered on FAVR, TSL, and CEF (Gu et al., 3 Aug 2025).