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REVIEW 3 major objections 4 minor 28 references

Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Fusing radiomic features with deep image representations estimates fetal gestational age with a mean absolute error of 8.0 days.

desk verdict The 8-day MAE is an artifact of label leakage—the GA target is computed from the same ROI contour that supplies the radiomic shape features—so the headline result does not establish clinically valid GA estimation. read the letter →

arxiv 2506.20407 v2 pith:MLXOICOT submitted 2025-06-25 eess.IV cs.CV

Fangyijie Wang , Yuan Liang , Sourav Bhattacharjee , Abey Campbell , Kathleen M. Curran , Guénolé Silvestre This is my paper · ORCID
classification eess.IVcs.CV
keywords gestationalageestimationfetalultrasoundradiomicsdeeplearningfeaturefusioncross-attentionheadcircumferenceinterpretability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to show that gestational age can be estimated automatically from fetal head ultrasound images and their region-of-interest masks by fusing two kinds of features: hand-crafted radiomic features that describe shape, intensity, and texture, and deep representations learned by a convolutional network. The authors report a mean absolute error of 8.0 days across three trimesters on two public datasets, which they claim outperforms current machine-learning-based methods for these gestational ages. The practical goal is an operator-independent dating tool that does not require manual biometric measurements and that offers a degree of interpretability through the radiomic features.

What carries the argument

The central mechanism is the cross-attention fusion module, which projects the standardized radiomic feature vector into queries and the deep image representation into keys and values, then applies scaled dot-product attention to produce a fused representation that a two-layer MLP regresses to gestational age. The radiomic features consist of 95 shape, first-order, and texture descriptors extracted from the fetal head region of interest, and the deep representation comes from a ConvNeXt convolutional network pre-trained on ImageNet and fine-tuned on the ultrasound data. The cross-attention module is the component that the ablation study credits for the accuracy gain: without it, concatenation gives no improvement over the image-only baseline, while adding it lowers the mean absolute error from 8.6 to 8.0 days.

What would settle it

Calculate the mean absolute error of the framework after removing all shape-based radiomic features; if the 8.0-day accuracy substantially degrades, the result is largely explained by the model reading the contour that defines the label. A second falsifier is to evaluate against first-trimester or known-conception gestational age: if the 8.0-day error does not survive an independent dating standard, the claimed gestational age accuracy is an artifact of the formula-derived target.

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Extended reading notes

Core claim

The paper's central claim is that a cross-attention fusion of deep image features and radiomic features yields more accurate gestational age estimates than either feature type alone, simple concatenation, or the underlying CNN by itself. In their experiments, the ConvNeXt-based image model fused with 95 radiomic features through cross-attention reaches a mean absolute error of 8.0 days, improving on the same model without fusion by 0.6 days and on the ResNet18 baseline by 1.9 days, and the cross-attention fusion outperforms concatenation across all tested architectures. The authors interpret this as evidence that radiomic features, which are inherently interpretable, complement deep representations for fetal brain maturation.

Load-bearing premise

The paper assumes that gestational age computed from head circumference via the INTERGROWTH-21st formula, with the head circumference derived from the annotated ROI contour in the same image, is a valid ground truth for measuring gestational age estimation accuracy.

Editorial extensions

If this is right

  • Automated gestational age dating becomes feasible without manual biometry, potentially reducing operator dependence in ultrasound screening.
  • The cross-attention fusion module is plug-and-play and improves accuracy across several CNN backbones, so it could be transferred to other fetal planes or other imaging tasks.
  • Because radiomic features drive part of the prediction, the model offers a route to explainable dating, with attention weights indicating which shape or texture properties matter.
  • The authors state the framework can be extended to other fetal anatomies such as the abdomen and femur, using the same fusion strategy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the gestational age labels are computed from the same head-circumference contour that feeds the radiomic shape features, a direct test would be to remove the shape features and see whether the fusion advantage persists; if it does not, part of the 8.0-day accuracy may be the model recovering the label formula rather than learning image-based dating.
  • A stronger clinical validation would compare the framework's estimates against first-trimester crown-rump length dating or known conception dates, which would tell whether the method adds information beyond reproducing the INTERGROWTH-21st curve.
  • The cross-attention improvement might also be tested with an ablation that replaces radiomic queries with random features to check how much of the gain comes from the specific radiomic content rather than the attention mechanism itself.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a feature-fusion framework for gestational age (GA) estimation from 2D fetal head ultrasound images, combining deep representations from a ConvNeXt backbone with 95 pyradiomics features extracted from an associated ROI mask, fused through a cross-attention module. The authors evaluate on the ES-TT and HC18 datasets, computing each image's GA label from head circumference (HC) obtained as the pixel count along the ROI contour times the pixel size, followed by the INTERGROWTH-21st formula. They report a best mean absolute error (MAE) of 8.0 days and claim superiority over image-only deep-learning baselines and radiomics-only machine-learning baselines, with code publicly available.

Significance. If the evaluation were valid, the framework could offer an interpretable, automatic alternative to manual biometry. The paper ships public code, compares several CNN/ViT backbones, and includes an ablation of the cross-attention module. However, the ground-truth label is constructed from the same ROI contour that generates the radiomic shape features used as input. The central quantitative claim is therefore not supported: the reported MAE largely measures how well the model can exploit a contour-derived measurement to invert a deterministic formula, rather than how well it estimates true clinical GA. The comparison to image-only baselines is also confounded by the additional mask input. These issues make the headline result uninterpretable as a statement about estimating gestational age in practice.

major comments (3)
  1. [Section 3.1] The GA label is defined by y = exp[0.05970 × (log_e(HC))^2 + 0.000000006409 × HC^3 + 3.3258] with HC = pnum × psize, where pnum is the number of pixels along the edge of the ROI mask X_ROI. The same X_ROI is used in Section 2.3 to extract 95 radiomic features that include Shape2D/perimeter descriptors. Thus the target is derived from the same contour that supplies a dominant input-feature family. Even though psize is not part of the explicit feature vector, the model has direct access to the contour from which HC is computed, and a model that reads contour length can reproduce the label once it learns the per-image scalar. The paper reports no control experiment with perimeter-only features, with all shape features withheld, or with masks withheld. The 8.0-day MAE therefore cannot be interpreted as evidence of clinically meaningful GA estimation; it is at least in part a test of whether the model can decode the HC-derived formula from the annotation.
  2. [Section 3.3, Table 1] The baseline deep-learning models are trained on ultrasound images only, whereas the proposed framework receives both the image and an expert-annotated ROI mask, from which radiomics are computed. This is not an apples-to-apples comparison: part of the reported improvement over ResNet18 (from 9.9 to 8.0 days) may simply reflect access to the mask rather than the fusion mechanism. The authors should evaluate image-only, mask-only, and image-plus-mask variants of the same fusion pipeline, and ideally an automatic-segmentation setting. Without such matched baselines, the claim of superiority over image-based methods is unsubstantiated. The abstract's statement that the method works 'without any measurement information' also conflicts with the use of expert ROI masks as inputs.
  3. [Section 3.1 and Section 3.3] The paper's across-trimesters claim is based on formula-derived GA from ROI contours, not on any externally established gestational age. For HC18, the dataset provides HC values, but the authors recompute HC from the contour and use that to form the target rather than validating against the provided HC or against a clinical reference such as first-trimester crown-rump length or last menstrual period. Consequently, the conclusion that the method 'outperforms current machine learning-based methods at these gestational ages' is not supported for actual pregnancy dating; it is a statement about fitting the INTERGROWTH-21st formula to contour lengths. An external evaluation with independently dated pregnancies is required before any clinical claim can be made.
minor comments (4)
  1. [Section 3.3, Table 2] The paragraph preceding Table 2 says the ML models are evaluated 'without using the radiomic features,' but the experimental setup in Section 2.5 and the table itself indicate that these models use radiomic features exclusively; this wording should be corrected.
  2. [Section 3.3, Table 1] The P-value column contains entries only for a few rows, with no description of the statistical test, the number of repeated runs, or multiple-comparison correction; the table should either present complete test information or omit the column.
  3. [Fig. 3 caption] The abbreviations IDMN, SDHGE, SAGLE, SRHGLE, and HGLE are not defined in the caption or in the text; they should be expanded or referenced to the pyradiomics documentation.
  4. [Section 3.1, HC18 dataset] Since HC18 provides HC values in millimeters, the manuscript should state whether the contour-derived HC used for the label matches the provided HC values, and if not, why the recomputation is preferable.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported 8.0-day MAE is partially circular: the GA target is computed from the same head-contour ROI mask that supplies the radiomic Shape2D features.

  1. self definitional [Section 3.1 'Formula Calculated GA'; Section 2.3 'Radiomic Features']
    "To obtain the GA for all images, we calculate the number of pixels, denoted as pnum, along the edge of the respective ROI in the input image XROI. Subsequently, we compute the corresponding HC in millimeters (mm) using the formula: HC = pnum × psize. We obtain the GA (y⟨i⟩) using the equation derived from previous research [21]: GA = exp[0.05970 × (loge(HC))2 + 0.000000006409 × (HC)3 + 3.3258]. Section 2.3: 'a total of 95 radiomic features xRAD ∈ R95 ... are extracted ... categorized into shape, statistical (first-order), and texture features.'"

    The ground-truth y is constructed as g(pnum × psize), where pnum is the number of edge pixels of X_ROI. The model's radiomic input includes Shape2D features computed from the same X_ROI contour, so the contour length that defines the label is present in the feature set (up to the per-image psize). The 'prediction' of GA is therefore reducible to reading the contour-derived shape feature and applying the INTERGROWTH-21st formula; the reported 8.0-day MAE is a measure of how well the fusion model reproduces its own input-derived label, not an independent estimate of gestational age. The abstract's 'without any measurement information' is contradicted because the annotated ROI mask is an explicit model input.

full rationale

The central circular step is in the construction of the ground truth: gestational age is not an independent clinical measurement but a deterministic transformation of head circumference, where HC is obtained from the pixel count along the edge of the same ROI mask used to compute radiomic Shape2D features. This makes the headline result partially self-referential: the fusion model has direct access, through the radiomics branch, to the contour information that generates the label. The image-only deep baselines (e.g., ConvNeXt MAE 8.6 days) are not circular in the same way because they do not consume the mask, so the paper retains some independent content. However, the best reported MAE (8.0 days) uses the mask-derived radiomic features on a mask-derived label, and the abstract's claim of estimating GA 'without any measurement information' is inaccurate since expert-drawn ROI masks are used as inputs. No load-bearing self-citation or imported uniqueness theorem is present; the circularity is confined to the label/input construction. Score 6 reflects a partial reduction-by-construction of the main reported prediction.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the assumption that formula-derived GA from HC is a valid ground truth, and that the same ROI masks can be used both to produce the label and to extract radiomic features. These are domain assumptions with no external validation. Hyperparameters are standard and not swept.

free parameters (3)
  • Learning rate = 1e-5
    Set in Section 3.2 without sensitivity analysis.
  • Cross-attention embedding size = 512
    Set in Section 2.4 without ablation.
  • Input image resolution = 256x256
    Used for both images and masks in Section 3.2; affects radiomic feature values but no resolution study is reported.
assumptions (3)
  • domain assumption GA for every image is obtained from HC via the INTERGROWTH-21st formula: GA = exp(0.05970(log_e HC)^2 + 0.000000006409(HC)^3 + 3.3258).
    Used as the ground truth label in Section 3.1; no external clinical GA is used.
  • domain assumption The pixel count pnum along the ROI contour, multiplied by psize, equals the head circumference.
    Section 3.1; assumes the annotated mask contour represents the fetal head and that psize calibration is correct.
  • domain assumption Radiomic features extracted from the resized 256x256 mask remain informative of the original mask's HC.
    Sections 2.3 and 3.2; features are taken after resizing while labels use original masks.

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Cite this review

Pith. "Pith review of Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images." pith.science (2026). https://pith.science/paper/MLXOICOT

@misc{pith2026250620407,
  author       = {Pith},
  title        = {Pith review of: Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MLXOICOT}},
  note         = {Machine review of arXiv:2506.20407}
}
read the original abstract

Accurate gestational age (GA) estimation, ideally through fetal ultrasound measurement, is a crucial aspect of providing excellent antenatal care. However, deriving GA from manual fetal biometric measurements depends on the operator and is time-consuming. Hence, automatic computer-assisted methods are demanded in clinical practice. In this paper, we present a novel feature fusion framework to estimate GA using fetal ultrasound images without any measurement information. We adopt a deep learning model to extract deep representations from ultrasound images. We extract radiomic features to reveal patterns and characteristics of fetal brain growth. To harness the interpretability of radiomics in medical imaging analysis, we estimate GA by fusing radiomic features and deep representations. Our framework estimates GA with a mean absolute error of 8.0 days across three trimesters, outperforming current machine learning-based methods at these gestational ages. Experimental results demonstrate the robustness of our framework across different populations in diverse geographical regions. Our code is publicly available on \href{https://github.com/13204942/RadiomicsImageFusion_FetalUS}.

Figures

Figures reproduced from arXiv: 2506.20407 by the authors.

Figure 1
Figure 1. Proposed fusion framework for GA estimation from fetal ultrasound images. Blue Box: Trainable parameters (DL model, cross-attention, MLP). Gray Box: Non￾trainable parameters (Radiomics). 2.2 Deep Representation Learning In our proposed framework, we employ the CNN topology to construct the DL model fθ to learn deep representations that reflect high-level semantic informa￾tion of ultrasound images, where, θ represent… view at source ↗
Figure 2
Figure 2. Visualization of CNBlock and Cross-attention module. (a) is an example of CNBlock with channel size 96; (b) represents the cross-attention module for integrating radiomic features and deep representations. LN: Layer Normalization has dimensions of 1 × nh × nw, where nh and nw correspond to the height and width of ROI image. The process of extracting radiomic features utilizes each pair of XUS and XROI as input. In t… view at source ↗
Figure 3
Figure 3. The visualization of the interpretability of our method. IDMN: Inverse Dif￾ference Moment Normalized. SDHGE: Small Dependence High Gray Level Emphasis. SAGLE: Small Area High Gray Level Emphasis. SRHGLE: Short Run High Gray Level Emphasis. HGLE: High Gray Level Emphasis [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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