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arxiv: 2605.28161 · v1 · pith:SJVHERAJnew · submitted 2026-05-27 · 💻 cs.CV

MeniOmni: A Structured Multimodal Benchmark for Holistic Meniscus Injury Assessment

Shurui Xu , Siqi Yang , Weiping Ding , Hui Wang , Mengzhen Fan , Yuyu Sun , Shuyan Li This is my paper

Pith reviewed 2026-06-29 13:23 UTC · model grok-4.3

classification 💻 cs.CV
keywords meniscus injurymultimodal benchmarkknee MRIStoller gradingclinical priorsdiagnostic report generationmedical imaging evaluationAI for radiology
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The pith

A benchmark pairing MRI scans with patient details shows that clinical context improves meniscus injury grading and cuts severe mistakes.

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

The paper presents MeniOmni, a collection of 746 multi-center knee MRI studies that supplies volumetric images together with patient information and expert text to test integrated diagnosis of meniscus tears. It defines two concrete tasks—fine-grained Stoller severity grading and structured report generation—plus evaluation metrics that treat clinically dangerous errors as more costly. Baseline runs indicate that feeding the patient context into models raises grading accuracy and lowers the rate of high-severity misclassifications compared with image-only inputs.

Core claim

MeniOmni supplies 746 tri-planar volumetric MRI studies, Clinical Priors such as sex age and BMI, and expert-annotated clinical text. The benchmark supports Stoller severity grading and diagnostic report generation, evaluated with risk-aware ordinal metrics and a semantic consistency score called Meni-Score. Experiments demonstrate that models given the Clinical Priors outperform image-only baselines and produce fewer severe grading errors.

What carries the argument

MeniOmni benchmark that supplies tri-planar MRI volumes together with Clinical Priors for joint evaluation of grading accuracy and report quality.

If this is right

  • Models supplied with Clinical Priors achieve higher accuracy on Stoller grading than image-only models.
  • The rate of severe grading errors drops when patient context is included.
  • Multimodal inputs enable evaluation of clinical reasoning that combines image and non-image evidence.
  • The two tasks and new metrics provide a structured way to compare systems on report generation quality.

Where Pith is reading between the lines

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

  • Similar multimodal pairing of scans and priors could be applied to other joint structures or to ligament and cartilage assessment.
  • The reported gains would need confirmation on data from additional scanner vendors or patient populations.
  • If the pattern holds, clinical systems might reduce diagnostic variability by routinely surfacing patient context alongside images.

Load-bearing premise

The expert-provided Stoller grades and clinical text annotations on these 746 studies serve as reliable ground truth for measuring holistic reasoning performance.

What would settle it

Re-running the baselines on a fresh set of cases where final clinical outcomes or independent radiologist re-reads replace the original annotations would show no accuracy gain or error reduction from adding Clinical Priors.

Figures

Figures reproduced from arXiv: 2605.28161 by Hui Wang, Mengzhen Fan, Shurui Xu, Shuyan Li, Siqi Yang, Weiping Ding, Yuyu Sun.

Figure 1
Figure 1. Figure 1: Overview of the MeniOmni Benchmark construction and supported [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Schematic illustration of the proposed content-adaptive MRI slice [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Analysis. The integration of clinical priors (Age: 26) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Clinical diagnosis of meniscus injuries requires radiologists to integrate volumetric MRI evidence with patient context (e.g., sex, age, BMI) and to produce structured diagnostic reports. Existing knee MRI benchmarks are typically unimodal and rely on coarse labels, limiting their ability to evaluate holistic clinical reasoning. We introduce MeniOmni, a structured multimodal benchmark for meniscus injury assessment, consisting of 746 multi-center MRI studies with tri-planar volumetric inputs, Clinical Priors, and expert-annotated clinical text. MeniOmni supports two tasks: (1) fine-grained Stoller severity grading and (2) diagnostic report generation. We further propose risk-aware ordinal evaluation and a semantic consistency metric (Meni-Score) to better reflect clinical relevance. Baseline experiments show that incorporating Clinical Priors improves grading performance and reduces severe errors, highlighting the value of multimodal context for safer assessment. Code and data are available at https://github.com/ShuruiXu/MeniOmni.

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

2 major / 2 minor

Summary. The paper introduces MeniOmni, a structured multimodal benchmark consisting of 746 multi-center knee MRI studies with tri-planar volumetric inputs, clinical priors (e.g., sex, age, BMI), and expert-annotated clinical text. It defines two tasks—fine-grained Stoller severity grading and diagnostic report generation—along with risk-aware ordinal evaluation and a semantic consistency metric (Meni-Score). Baseline experiments indicate that incorporating clinical priors improves grading performance and reduces severe errors.

Significance. If the annotations are shown to be reliable, the benchmark could advance multimodal medical AI by supplying structured data and clinically aligned metrics that go beyond existing unimodal knee MRI datasets. The explicit release of code and data is a clear strength supporting reproducibility.

major comments (2)
  1. Dataset construction (as described in the abstract and implied methods): No inter-annotator agreement, annotation protocol details, or correlation with surgical findings are reported for the expert-annotated Stoller grades and clinical text on the 746 studies. This directly undermines the central claim that 'incorporating Clinical Priors improves grading performance and reduces severe errors,' because measured gains could arise from fitting to unvalidated or inconsistent labels rather than genuine multimodal reasoning.
  2. Baseline experiments (abstract): The description provides no information on model architectures, data splits, statistical significance tests, or controls for selection biases. These omissions are load-bearing for interpreting the reported improvements from clinical priors and prevent verification of the soundness of the empirical results.
minor comments (2)
  1. Clarify the exact variables included in 'Clinical Priors' beyond the examples given, and provide the precise definition or formula for the Meni-Score metric to support reproducibility.
  2. Add a dedicated comparison table against existing knee MRI benchmarks to highlight differences in modality, label granularity, and evaluation approach.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive feedback. We address the major comments point-by-point below, clarifying the manuscript content and indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: Dataset construction (as described in the abstract and implied methods): No inter-annotator agreement, annotation protocol details, or correlation with surgical findings are reported for the expert-annotated Stoller grades and clinical text on the 746 studies. This directly undermines the central claim that 'incorporating Clinical Priors improves grading performance and reduces severe errors,' because measured gains could arise from fitting to unvalidated or inconsistent labels rather than genuine multimodal reasoning.

    Authors: We agree that explicit documentation of the annotation process is essential for a benchmark paper. The full manuscript states that Stoller grades and clinical text were produced by board-certified radiologists following established clinical criteria, with a high-level description of the protocol. However, inter-annotator agreement statistics and correlation with surgical findings were not collected during dataset creation. We will add a dedicated subsection detailing the annotation workflow and will revise the discussion to explicitly note these validation limitations. The reported improvements from clinical priors are empirical observations on the given labels; we will qualify the central claim to reflect the current level of label validation. revision: partial

  2. Referee: Baseline experiments (abstract): The description provides no information on model architectures, data splits, statistical significance tests, or controls for selection biases. These omissions are load-bearing for interpreting the reported improvements from clinical priors and prevent verification of the soundness of the empirical results.

    Authors: The full manuscript contains dedicated experimental sections that specify the multimodal model architectures, the train/validation/test splits (stratified by center to address selection bias), and the statistical tests used to assess improvements. These details were omitted from the abstract for brevity. We will expand the abstract and methods to include this information explicitly and will add a short paragraph on bias controls. revision: yes

standing simulated objections not resolved
  • Inter-annotator agreement scores and correlation with surgical findings were never collected, so numerical values for these cannot be supplied.

Circularity Check

0 steps flagged

No circularity: benchmark paper with no derivations or fitted predictions

full rationale

The manuscript introduces MeniOmni as a dataset and benchmark supporting two tasks (Stoller grading and report generation) together with baseline experiments and new metrics. No equations, parameter fits, uniqueness theorems, or predictions appear anywhere in the provided text. All reported results are direct empirical measurements on the released 746-study collection; the central claim that Clinical Priors improve performance is therefore an observable outcome rather than a quantity derived from itself. No self-citations are invoked to justify any load-bearing step.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a benchmark creation paper; no free parameters, axioms, or invented entities are introduced beyond standard practices in dataset curation and evaluation metric design.

pith-pipeline@v0.9.1-grok · 5715 in / 1176 out tokens · 36377 ms · 2026-06-29T13:23:36.010659+00:00 · methodology

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Reference graph

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