Cross-modal Medical Image Generation Based on Pyramid Convolutional Attention Network

Chao Yang; Dong Dai; Fuyou Mao; Hao Zhang; Lixin Lin; Ming Jiang; Yan Tang

REVIEW 4 major objections 6 minor 28 references

Cross-modal Medical Image Generation Based on Pyramid Convolutional Attention Network

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A GAN that generates PET images from structural MRI beats previous methods on ADNI quality and AD diagnosis.

desk verdict A plausible architecture package, but the headline numbers are effectively unreviewable: no held-out split, an adversarial loss written as L1, and baselines only partially reproduced. read the letter →

arxiv 2411.17420 v2 pith:CJ2UXMSQ submitted 2024-11-26 cs.CE eess.IV

Fuyou Mao , Lixin Lin , Ming Jiang , Dong Dai , Chao Yang , Hao Zhang , Yan Tang This is my paper · ORCID

classification cs.CEeess.IV

keywords Alzheimer'sdiseasecross-modalmedicalimagegenerationPETsynthesisfromMRIpyramidconvolutionchannelattentionself-attentiongenerativeadversarialnetworkADNIdataset

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

The paper aims to show that missing PET images can be synthesized from structural MRI scans well enough for clinical and diagnostic use. The proposed generative model, PCSA-GAN, combines pyramid convolution with channel attention to capture multi-scale local features from sMRI, and injects global relationships with self-attention before reconstructing the PET image. On the ADNI dataset the authors report a mean absolute error of 0.0194, a peak signal-to-noise ratio of 29.65, and structural similarity of 0.9486, all better than the previous methods they compared against. When the generated PET images are combined with the original sMRI in an Alzheimer's disease classifier, they report 94.21% accuracy, above sMRI alone and above prior sMRI-to-PET pipelines. If these results hold, the method would make multimodal diagnosis feasible in settings where PET is unavailable.

What carries the argument

The load-bearing mechanism is the PCCA block (pyramid convolution plus channel attention) in the generator's contraction path, followed by a self-attention layer before the deconvolution stages. Pyramid convolution runs several 3D convolutions with different kernel sizes in parallel and concatenates their output, so the network sees both fine details and broad context; channel attention reweights those multi-scale features to suppress redundancy. Self-attention then computes global correlations across spatial positions of the deepest feature map, which the authors argue preserves the overall structure of the brain. The generator is trained with a joint loss combining adversarial loss, voxel-wise L1 loss, and multi-scale SSIM loss, each responsible for a different aspect of fidelity.

What would settle it

Rerun the three main models (PCSA-GAN, HGAN, and GLA-GAN) on a fixed public train/test split of the 356 paired ADNI subjects, using the authors' preprocessed 64x64x64 gray-matter masks, and check whether PCSA-GAN still gets the best MAE, PSNR, and SSIM on the same held-out test set; if it does not, the outperformance claim collapses.

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Extended reading notes

Core claim

The central claim is that a U-Net-style conditional GAN whose encoder uses pyramid convolution (parallel 3D convolutions with kernels of size 7, 5, and 3) plus channel attention, and whose decoder begins with a self-attention layer, produces PET images that quantitatively and visually match real FDG-PET scans better than earlier CNN-, U-Net-, and CycleGAN-based sMRI-to-PET methods. The authors report MAE 0.0194, PSNR 29.65, and SSIM 0.9486 on the ADNI dataset, and they report that these synthesized PET images, paired with the source sMRI, give 94.21% accuracy in an AD-versus-normal classification task. They also demonstrate via ablation that removing either the pyramid-channel-attention module or the self-attention module degrades the metrics, and that adding L1 and multi-scale SSIM losses on top of adversarial loss improves output quality.

Load-bearing premise

The headline numbers rest on the assumption that the test subjects were not used in training and that HGAN and GLA-GAN were run under the same conditions, but the paper admits in Section 4.8 that it did not fully reimplement those methods.

Editorial extensions

If this is right

Generated PET could substitute for real PET in downstream multimodal classification, raising accuracy from 90.48% with sMRI alone to 94.21% in the paper's experiments.
Clinics without PET capacity could still obtain PET-equivalent metabolic information from the MRI scans they already collect.
The architecture suggests that multi-scale local features plus global self-attention is a workable recipe for cross-modal medical image synthesis.
The joint loss result implies that voxel-level and structural losses add complementary training signal to adversarial loss for medical image generation.

Reading between the lines

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

Editorial inference: if the reported results replicate on an independent patient-disjoint split, the method could reduce the need for real PET scans in AD research cohorts, since the synthesized PET carries most of the classification information.
Editorial inference: the same pyramid-plus-attention generator could be tried for other missing-modality pairs such as MRI-to-CT or across PET tracers, since the method is not tied to AD-specific image content.
Editorial inference: the strongest public test would be to release train/test subject identifiers and the reimplemented baseline code, allowing the outperformance claim to be checked without relying on the paper's in-house reproduction.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take on the PCSA-GAN manuscript. The architecture is a reasonable, incremental combination: pyramid convolution with channel attention for multi-scale local features, self-attention for global context, and a joint loss with MS-SSIM. As a package, I don't think this exact configuration has been published before, and the ablations suggest each block contributes something. The preprocessing pipeline is described in enough detail that a determined reader could roughly replicate it.

But the evaluation does not support the headline claim. Section 4.3 reports an MAE of 0.0194, PSNR 29.65, and SSIM 0.9486 "during training" on ADNI. There is no train/test split for the 356 paired subjects, no separate validation procedure, and no error bar for the final model on a fixed held-out set. The std devs in Tables 1 and 2 come from varying architecture or loss, not from repeated trials on a fixed test set. So the "outperforms previous methods" claim is not verifiable. The baseline comparison is also fragile: Section 4.7 says HGAN and GLA-GAN were "attempted to reproduce" from their papers, and Section 4.8 admits full reimplementation was not completed.

The loss function section compounds the problem. Eq. 7 is labeled "adversarial loss" but is numerically identical to the voxel-wise L1 in Eq. 8. And Eq. 12 includes an LD term that is never defined. That's not a typo-level issue; it means the training objective is ambiguous. There are also smaller signs of carelessness: Table 2 is titled "The impact of the self-attention mechanism" but reports loss-function ablations, and several references don't match the method they are meant to support ([24] is about anterior commissure anatomy, not the N3 algorithm; [27] and [28] are similarly off-target).

I want to give credit where it's due: the idea is plausible, the architecture description is mostly clear, and the authors identify a real clinical problem. But for an empirical paper, the evidence needs to be reproducible. A serious referee would hit the same wall within an hour. I would not send this to review in its current form. The right next step is a revision with a proper held-out protocol, code or data release, and a corrected loss description. If that lands, it could be a solid incremental contribution, but right now the claims outrun the evidence.

Referee Report

4 major / 6 minor

Summary. The paper proposes PCSA-GAN, a conditional GAN with a U-Net style generator that augments pyramid convolution with channel attention (PCCA blocks) and a self-attention layer for generating PET images from sMRI in Alzheimer's disease research. The method is evaluated on ADNI, reporting MAE 0.0194, PSNR 29.65, and SSIM 0.9486 for image generation, and 94.21% accuracy for AD classification when the generated PET is combined with sMRI. The authors claim these results outperform prior sMRI-to-PET synthesis methods.

Significance. If properly validated, the proposed architecture would be a modest but useful contribution to cross-modal medical image synthesis, combining multi-scale local feature extraction with global self-attention. The paper includes ablation studies over the PCA and SA modules and over loss functions, which is a strength, and it reports standard deviations for these ablations. However, the empirical claims are currently not supported because the evaluation protocol lacks a held-out test split, the baseline comparisons are incomplete, and the training objective is ambiguously specified. The paper also does not provide code or data to enable independent verification, limiting its reproducibility.

major comments (4)

[Section 4.2, 4.3] The paper never specifies a train/test split for the 356 paired subjects. Section 4.3 reports the headline metrics 'during training on the publicly available ADNI dataset,' which suggests the MAE, PSNR, and SSIM values may be computed on the training set. Without a held-out test partition, these numbers do not demonstrate generalization, and the claimed superiority over prior methods cannot be assessed. This is a load-bearing issue for the central empirical claim.
[Section 3.4.1, 3.4.2, Eq. (7)-(12)] Equation (7) is labeled 'adversarial loss' but is identical to the voxel-wise L1 reconstruction loss in Eq. (8). The discriminator loss L_D that appears in Eq. (12) is never defined. The training objective is therefore ambiguous, and it is unclear how adversarial training contributes to the reported results. The authors should state the actual adversarial loss and the full training objective.
[Section 4.7, 4.8] The comparison with HGAN and GLA-GAN is not verifiable. The authors state that they 'attempted to reproduce' these models and admit in Section 4.8 that it is 'not easy to implement these methods completely.' Without a complete, common implementation and a shared evaluation protocol, the reported superiority is unsupported. Additionally, the 328 sMRI-only subjects used for the classification experiment are not shown to be disjoint from the 356 paired subjects used to train the generator; if there is overlap, the classification results are optimistically biased.
[Table 3, Section 4.8] Table 3 shows that the proposed method has a lower AUC (94.91%) than Pan et al. (96.95%) and Gao et al. (95.60%), yet the text in Section 4.8 and the abstract claim the method outperforms previous studies in AD diagnosis. The claim should be restricted to the metrics where the method is actually best (ACC, SPE, SEN, F1), or the AUC discrepancy must be explained. As written, the claim is internally inconsistent with the reported numbers.

minor comments (6)

[Table 2 caption] The caption of Table 2 repeats the caption of Table 1 and refers to 'the impact of the self-attention mechanism,' but the table rows list loss functions. The caption should be corrected.
[Section 4.8] The text contains 'as shown in Table ??' instead of a proper reference to Table 3.
[Section 3.4.2, Eq. (3), (9), (10)] There are typesetting errors in equations: Eq. (3) is missing the pooling and activation symbols, and Eqs. (9) and (10) have misplaced or mismatched brackets. These should be cleaned up.
[References] Reference [24] is cited for the N3 bias-correction algorithm but points to a paper about the anterior commissure; reference [27] is cited for the HAMMER registration algorithm but the cited work is on histopathological image registration. The citations should be corrected to the original methodological sources.
[Throughout] The paper uses both 'MS-SSIM' and 'MM-SSIM' (the latter in Table 2). The abbreviation should be unified to 'MS-SSIM'.
[Table 1, Section 4.5] The rows of Table 1 are labeled 'Patch Size,' but the text describes applying self-attention at different scales of features. Please clarify what 'Patch Size' means and how it relates to the feature scale.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is an empirical GAN benchmarking paper whose claims rest on measured comparisons against ground-truth PET on ADNI, not on a derivation chain that reduces to its own inputs.

full rationale

The paper contains no derivation chain of the kind that could be circular. The central claims—that PCSA-GAN produces PET images with MAE 0.0194, PSNR 29.65, and SSIM 0.9486, and that generated PET plus sMRI yields 94.21% AD classification accuracy—are empirical measurements against real PET ground truth and a classification task, not quantities forced by construction from the method's definitions. The identical equations for adversarial loss (Eq. 7) and voxel-wise L1 loss (Eq. 8) are a typographical duplication of the same formula, not a case of a fitted input being renamed as a prediction. The self-attention patch size and loss-function choices were compared on the evaluation set (Tables 1 and 2), which is a selection-bias or reporting-validity concern, not circularity under the enumerated definitions. No load-bearing argument relies on a self-citation or on an imported uniqueness theorem; references to prior GAN methods are contextual comparisons, and Section 4.8's admission that previous methods were not fully reimplemented weakens the comparative claim but does not make the paper's own result identical to its assumptions. The paper is self-contained as an empirical study, so the honest finding is no significant circularity.

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

The paper does not introduce new physical entities or a mathematical derivation. Its central claim rests on empirical GAN training, and the ledger captures the manual hyperparameters and the implicit data processing assumptions that are load-bearing for the reported metrics. The most serious items are the undisclosed loss weights, the test-set-based selection of patch size, and the unstated train/test split.

free parameters (3)

Loss weighting hyperparameters alpha, beta, gamma = not reported
Eq. (12) defines joint loss L = alpha*LD + beta*LG1 + gamma*LG2 and states these are manually set, but no values are given. The final metrics depend on these choices.
Self-attention patch size = 4x4x4
Section 4.5 compares patch sizes 4x4x4, 16x16x16, and 32x32x32, and the best (4x4x4) is used for final results. This selection is made on the evaluation set without a described validation split.
Number of training epochs = 1000
The paper fixes 1000 epochs without describing early stopping or a selection criterion, so the stopping point is a free choice.

assumptions (3)

domain assumption Gray matter (GM) masks of sMRI contain sufficient information to predict FDG-PET uptake.
Section 4.1 selects GM masks because 'GM demonstrated higher relatedness to AD', but the cited references do not support the specific claim that GM intensity patterns determine PET uptake. No quantitative evidence is given for this premise.
domain assumption ADNI sMRI and FDG-PET pairs are correctly co-registered and preprocessed into a common 64x64x64 space so that voxel-wise losses are meaningful.
Section 4.1 describes registration and downsampling; any residual misalignment directly penalizes L1 and MS-SSIM losses and would distort the reported MAE, PSNR, and SSIM.
domain assumption A single train/test split exists and is consistent across all compared methods.
The paper never states how the 356 paired subjects and 328 sMRI-only subjects are split. Without this, all reported metric comparisons lack a defined evaluation protocol.

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Cite this review

Pith. "Pith review of Cross-modal Medical Image Generation Based on Pyramid Convolutional Attention Network." pith.science (2026). https://pith.science/paper/CJ2UXMSQ

@misc{pith2026241117420,
  author       = {Pith},
  title        = {Pith review of: Cross-modal Medical Image Generation Based on Pyramid Convolutional Attention Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CJ2UXMSQ}},
  note         = {Machine review of arXiv:2411.17420}
}

read the original abstract

The integration of multimodal medical imaging can provide complementary and comprehensive information for the diagnosis of Alzheimer's disease (AD). However, in clinical practice, since positron emission tomography (PET) is often missing, multimodal images might be incomplete. To address this problem, we propose a method that can efficiently utilize structural magnetic resonance imaging (sMRI) image information to generate high-quality PET images. Our generation model efficiently utilizes pyramid convolution combined with channel attention mechanism to extract multi-scale local features in sMRI, and injects global correlation information into these features using self-attention mechanism to ensure the restoration of the generated PET image on local texture and global structure. Additionally, we introduce additional loss functions to guide the generation model in producing higher-quality PET images. Through experiments conducted on publicly available ADNI databases, the generated images outperform previous research methods in various performance indicators (average absolute error: 0.0194, peak signal-to-noise ratio: 29.65, structural similarity: 0.9486) and are close to real images. In promoting AD diagnosis, the generated images combined with their corresponding sMRI also showed excellent performance in AD diagnosis tasks (classification accuracy: 94.21 %), and outperformed previous research methods of the same type. The experimental results demonstrate that our method outperforms other competing methods in quantitative metrics, qualitative visualization, and evaluation criteria.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [24]

In: Seminars in Cell & Developmental Biology, vol

Fenlon, L.R., Suarez, R., Lynton, Z., Richards, L.J.: The evolution, formation and connectivity of the anterior commissure. In: Seminars in Cell & Developmental Biology, vol. 118, pp. 50–59 (2021). Elsevier

work page 2021

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Pattern Recognition Letters 152, 210–217 (2021)

Chen, Z., Zhao, S., Hu, K., Han, J., Ji, Y., Ling, S., Gao, X.: A hierarchical and multi-view registration of serial histopathological images. Pattern Recognition Letters 152, 210–217 (2021)

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Brain Structure and Function 228(5), 1045–1067 (2023) 18

He, C., Guan, X., Zhang, W., Li, J., Liu, C., Wei, H., Xu, X., Zhang, Y.: Quantitative susceptibility atlas construction in montreal neurological institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification. Brain Structure and Function 228(5), 1045–1067 (2023) 18

work page 2023

[4] [1]

Neurocomputing 392, 296–304 (2020)

Bi, X., Li, S., Xiao, B., Li, Y., Wang, G., Ma, X.: Computer aided alzheimer’s disease diagnosis by an unsupervised deep learning technology. Neurocomputing 392, 296–304 (2020)

work page 2020

[5] [2]

Frontiers in bioengineering and biotechnology 8, 534592 (2020) 15

Folego, G., Weiler, M., Casseb, R.F., Pires, R., Rocha, A.: Alzheimer’s dis- ease detection through whole-brain 3d-cnn mri. Frontiers in bioengineering and biotechnology 8, 534592 (2020) 15

work page 2020

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Neuroimage 208, 116459 (2020)

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Biomedical Signal Processing and Control 80, 104312 (2023)

Rallabandi, V.S., Seetharaman, K.: Deep learning-based classification of healthy aging controls, mild cognitive impairment and alzheimer’s disease using fusion of mri-pet imaging. Biomedical Signal Processing and Control 80, 104312 (2023)

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Nature communications 13(1), 3404 (2022)

Qiu, S., Miller, M.I., Joshi, P.S., Lee, J.C., Xue, C., Ni, Y., Wang, Y., De Anda- Duran, I., Hwang, P.H., Cramer, J.A., et al.: Multimodal deep learning for alzheimer’s disease dementia assessment. Nature communications 13(1), 3404 (2022)

work page 2022