{"id":"418f25dc-f660-4500-b461-6baf1df7d96c","arxiv_id":"2412.03629","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"DiffuPT pretrains a glaucoma classifier on diffusion-generated, baseline-filtered synthetic fundus images and then fine-tunes on real images, improving harmonic mean from 89.09% to 92.59% on the new GlaucomaEgy test set.","lead":"This paper proposes DiffuPT, a pipeline that uses a latent diffusion model to generate synthetic fundus images, then pretrains a glaucoma classifier on the balanced synthetic set before fine-tuning on real data. On a newly collected Egyptian dataset and on AIROGS, the method raises the harmonic mean of sensitivity and specificity over standard training.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Unequal training budgets confound the comparison: DiffuPT uses 100k pretraining steps plus 60k fine-tuning steps, whereas the baseline and likely the compared imbalance methods are trained for 60k iterations, so the reported improvements may reflect extra compute rather than the synthetic-data…","rationale":"The reader's verdict is CONDITIONAL with correctness risk medium, and the reader noted 'compute mismatch' as one of the issues in the rationale but identified the filtering of synthetic images as the weakest assumption. I agree that the filtering mechanism is underexplained, but the more load-bearing concern is the unequal training budget: DiffuPT receives 160k total iterations versus 60k for baselines and likely for the compared methods. This is a standard confound that can fully explain the reported gains without invoking the method's novelty. It is concrete, testable, and directly attacks the central causal claim that synthetic-data pretraining is what drives the improvement. I therefore partially agree with the reader: the filtering concern is secondary; the compute confound is the primary obstacle to accepting the paper's claim. The paper deserves credit for collecting a large national dataset and for extensive comparisons across generation methods and imbalance techniques, but these strengths do not address the compute mismatch. A compute-matched control experiment is necessary before the DiffuPT mechanism can be validated. Since the reader already conditioned acceptance on addressing such issues, my concern does not move the verdict, but it sharpens the condition.","tokens_in":13338,"tokens_out":8906,"duration_ms":90674,"concrete_test":"Retrain the Swin Large baseline with the exact DiffuPT schedule on the real GlaucomaEgy training set: 100k iterations at lr 1e-4 followed by 60k iterations at lr 1e-5, with the same validation-based checkpoint selection and the same BCE loss, but without any synthetic images. Evaluate on the same test set. If this compute-matched baseline reaches a harmonic mean close to 92.59, the DiffuPT improvement is attributable to extra compute rather than to synthetic pretraining. Additionally, retrain the weighted-sampler comparator for 160k total iterations to check whether the gap closes. Report results over multiple seeds with confidence intervals.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 5.1 reports that baseline classifiers are trained for 60k iterations. Section 5.3 states that DiffuPT pretrains on generated data for 100k iterations and then fine-tunes for 60k iterations, for a total of 160k optimizer steps. The comparison tables (Tables 7 and 8) do not specify iteration counts for the other imbalance mitigation methods, but these methods use the same Swin backbone and are described in the context of the baseline training setup, so it is reasonable to infer they receive 60k steps. If so, DiffuPT receives 2.67x more training than every comparator. The reported gains (test harmonic mean 92.59 vs. 89.09 for normal training, 91.61 for weighted sampler, and 92.17 for augmentation) could therefore be explained by longer training alone, independent of the proposed generative pretraining. This confound directly threatens the central claim that diffusion-based balanced pretraining improves glaucoma classification. The filtering ablation (Table 9) does not resolve the issue because both arms use the same 160k budget. The close margin over the augmentation method (92.59 vs. 92.17) is particularly suspicious: the augmentation method uses the same generated images but presumably only 60k training steps. Additionally, Section 5.3 notes that the validation set was used to select the best pretrained weights, introducing a selection step that may not be matched in the comparators. Without a compute-matched baseline, the unique contribution of DiffuPT is not established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes DiffuPT, a training scheme for glaucoma classification that uses a latent diffusion model trained on imbalanced real fundus images to generate a balanced synthetic dataset, filters the generated samples with the baseline classifier, pretrains a Swin Transformer on the filtered synthetic data, and then fine-tunes on the real, imbalanced data. The authors introduce GlaucomaEgy, a large Egyptian fundus dataset (33,633 gradable images), and report that DiffuPT improves the harmonic mean from 89.09% to 92.59% on its test set, with similar gains on the AIROGS dataset. Extensive comparisons are presented against GAN-based and diffusion-based generation methods and against standard class-imbalance techniques.","tokens_in":13689,"tokens_out":3338,"duration_ms":34244,"significance":"Class imbalance is an important problem in medical image classification, and the idea of using diffusion-generated data for pretraining is timely and potentially useful. The main contribution is a large national dataset and a clearly described empirical framework. However, the current evidence is weakened by a training-budget confound (DiffuPT receives 160k iterations versus 60k for the baseline), a filtering step whose effect is unexplained, anomalous AUC values for two baseline methods, and the absence of repeated-run statistics. If the authors provide compute-matched baselines, a mechanistic analysis of the filter, and confidence intervals, the approach would represent a meaningful, practical contribution. The paper does not include code or reproducibility artifacts, and the single-run results should not be taken as definitive.","major_comments":[{"comment":"The central comparison is confounded by unequal training budgets. Section 5.1 states that baseline classifiers are trained for 60k iterations, while Section 5.3 states that DiffuPT uses 100k pretraining iterations followed by 60k fine-tuning iterations, for a total of 160k optimizer steps. The tables that report the headline results (Tables 7 and 8) do not specify the iteration counts of the other imbalance-mitigation methods, but these methods are described in the context of the baseline setup and thus likely also receive 60k steps. If so, DiffuPT receives 2.67x more training than every comparator, and the reported gains could be explained by additional training alone. The filtering ablation in Table 9 does not resolve this because both arms use the same 160k budget. To support the claim that diffusion-based balanced pretraining is beneficial, the authors must add a compute-matched control, e.g., training the baseline and each comparator for 160k iterations, or pretraining for 100k iterations on randomly shuffled real data and then fine-tuning for 60k.","section":"§5.1 vs §5.3; Tables 7, 8"},{"comment":"The filtering stage is load-bearing but unexplained. Section 4.2 states that generated images are \"filtered using our already trained baseline classifier\" to determine whether they belong to the desired class, and Table 9 shows that filtering improves the harmonic mean from 91.5 to 92.59. However, the manuscript never reports the filtering threshold, the fraction of generated samples removed, or what types of samples are removed. If the filter prunes hard, informative positives, the pretraining set becomes easy and reinforces the baseline's blind spots, which would invalidate the proposed mechanism. At minimum, the authors should report the filtering statistics, and ideally compare filtering by the baseline against filtering by an oracle or by another classifier to establish that the filter removes noise rather than informative positives.","section":"§4.2; Table 9"},{"comment":"The AUC values reported for the weighted-CE baselines are anomalous and undermine the comparison. In Table 7, Weighted CE achieves AUC 90.27 and Weighted CE + Sampler achieves 92.37, while every other method (including normal training) achieves AUC above 97. The same pattern appears in Table 8, with AUC 89.17 and 90.09 for those two baselines. Since class weighting changes the learned model and should not collapse AUC to this degree, these results suggest an implementation or evaluation issue (for example, a learning-rate mismatch or a bug in how predictions are aggregated). The authors must explain these anomalies or re-run the experiments; otherwise the comparisons against those baselines are not reliable.","section":"Tables 7 and 8"},{"comment":"No statistical significance or repeated runs are reported. All tables show a single value per metric, and the margins between DiffuPT and the best comparator are small on the test set (92.59 vs 92.17 harmonic mean for the augmentation method, 98.02 vs 97.40 AUC for the weighted sampler). Given the stochasticity of deep-network training, these differences may not be reproducible. The authors should run at least three to five independent seeds for each condition and report means and standard deviations or confidence intervals, particularly for the DiffuPT versus augmentation comparison that is closest.","section":"§5.3; Tables 7-10"},{"comment":"The pretraining checkpoint selection protocol may introduce another confound. Section 5.3 states: \"We used the same validation set to determine the best initial weights.\" This means the validation set is used to select among pretrained checkpoints before fine-tuning, a selection step that the compared methods may not have. If the comparators are not given the same validation-based selection, the comparison is not fair. The authors should either apply the same selection protocol to all methods or show that the result is robust to removing this selection step.","section":"§5.3"}],"minor_comments":[{"comment":"Figure 1 presents embedding analyses for 'normal training', 'pretraining', and 'fine-tuned' models, but the figure has no axis labels, no description of which layer's embeddings are visualized, and no details on the dimensionality-reduction technique; please add these details so the figure is interpretable.","section":"Figure 1"},{"comment":"The text says LDM is chosen because it had the best 'generation scores', however DDPM has the best Inception Score (188.39 vs 153.74) while LDM has better FID and KID; the choice of LDM should be justified explicitly in light of this trade-off.","section":"§5.2.2 and Table 5"},{"comment":"The autoencoder used in the LDM was trained 'on a collection of AIROGS and GlaucomaEgy datasets', but the manuscript does not state whether this training used the same train/validation/test splits or whether the test sets of either dataset were included; if the autoencoder saw test images, this would be a data-leakage risk that must be clarified.","section":"§4.1"},{"comment":"The header 'GlacomaEgy Validation' contains a typo; it should read 'GlaucomaEgy'.","section":"Table 7 header"},{"comment":"The AIROGS experiments do not specify how many generated samples were used for pretraining, whether the baseline classifier filtering was applied, or what the class distribution of the pretraining set was; please report these details so the setup is reproducible.","section":"§5.4 and Table 10"}],"recommendation":"major_revision","confidential_remarks":"The paper presents a potentially useful method and a valuable dataset, but the current empirical evidence is not fully convincing. The compute mismatch alone is enough to require major revision. In addition, the anomalous AUC values for the weighted-CE baselines suggest a possible implementation error in a core baseline; if the authors cannot explain these values, the comparisons in Tables 7 and 8 should be re-run. I would encourage the editor to ask for compute-matched controls and repeated-run statistics, and to consider whether the GlaucomaEgy dataset deserves separate recognition even if the DiffuPT claim remains unproven."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the new GlaucomaEgy dataset and the seven-way generative model comparison are genuinely useful, but the central claim that DiffuPT beats the imbalance baselines is not established because DiffuPT gets 2.67x more optimizer steps than the comparators. This needs a compute-matched rerun before I'd trust the headline numbers.\n\nWhat's good: collecting and grading 37k fundus images with a two-expert-plus-adjudicator protocol is real work, and the demographic breakdown (age, gender, diabetes, left/right) plus the grading mismatch analysis is more transparency than most dataset papers bother with. The systematic comparison of CycleGAN, StarGANv2, MW-GAN, UNSB, DDPM, and LDM on the same downstream task is also valuable; the FID/KID/IS table and the augmentation curves give practitioners a useful reference. The DiffuPT pipeline as a combination of LDM generation, baseline filtering, balanced pretraining, and fine-tuning is a legitimate new variant, and the pretraining distribution ablation (Table 6) is a nice touch.\n\nThe soft spots are mostly about the evidence for the headline gain. The stress-test note is right: Section 5.1 says baselines get 60k iterations, Section 5.3 says DiffuPT gets 100k pretraining plus 60k fine-tuning. That's 160k vs 60k, so the entire claimed improvement over normal training and the narrow 0.42 point margin over the augmentation method could just be extra training. The filtering ablation (Table 9) doesn't fix that because both arms use 160k. There's also no confidence intervals or multiple seeds anywhere, and the AUC numbers for weighted CE and weighted CE+sampler (89-92) are suspiciously lower than everything else (97-98); that's a red flag that something else is different about those runs, not just the loss. The filtering step is described only as 'filtered using our already trained baseline classifier' with no detail on the threshold, how many samples were removed, or what the failure modes look like, so we can't tell if it's pruning hard positives or just denoising. And no code or data release is mentioned, which matters for a dataset paper.\n\nBottom line: this is a solid empirical contribution in need of a serious referee, not a desk reject. The dataset alone justifies review. But the authors need to run compute-matched baselines, add error bars, explain the AUC anomalies, and release the dataset and filtering code before the DiffuPT claim is credible. I'd take it to a reading group, and I'd cite the dataset if I worked on glaucoma screening.","headline":"Useful dataset and thorough generative model comparison, but the central DiffuPT claim is confounded by a 2.7x training budget gap; worth serious review with compute-matched baselines.","tokens_in":14210,"tokens_out":2238,"would_cite":true,"duration_ms":21256,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A diffusion-based pretraining pipeline lifts glaucoma screening harmonic mean from 89.09% to 92.59% on a new Egyptian dataset.","keywords":["glaucoma detection","class imbalance","diffusion models","latent diffusion","synthetic data generation","pretraining","fundus images","Swin Transformer"],"falsifier":"A direct test: pretrain the same Swin Transformer on a balanced set of real glaucoma images (e.g., oversampled or additionally collected) with identical compute, learning rate, and iteration counts. If real-balanced pretraining matches or exceeds DiffuPT's 92.59% harmonic mean, then the diffusion-generated content is not the active ingredient. A second check is to measure the filter's rejection rate on generated glaucoma images and inspect what it rejects; if it systematically removes subtle or early-stage cases, the mechanism is suspect.","tokens_in":13139,"feed_emoji":"👁","tokens_out":7453,"duration_ms":63010,"temperature":0.7,"pith_summary":"The paper proposes DiffuPT, a training scheme that targets glaucoma's class imbalance by generating balanced synthetic fundus images with a class-conditioned latent diffusion model, filtering them with a baseline classifier, and using them to pretrain a Swin Transformer before fine-tuning on real data. The authors collected GlaucomaEgy, an Egyptian dataset of more than 37,000 gradable fundus images in which roughly 10 percent are glaucomatous. On that dataset's test set, they report the harmonic mean of sensitivity and specificity rising from 89.09% with normal training to 92.59% with DiffuPT, with AUC reaching 98.02%. They report similar improvements on the AIROGS dataset, arguing that balanced synthetic pretraining stabilizes representations and fine-tuning then recovers discriminative accuracy.","feed_headline":"Balanced synthetic images lift glaucoma classifier to 92.59%","feed_subtitle":"Diffusion-generated, baseline-filtered fundus images beat standard imbalance fixes in two datasets.","key_machinery":"The key machinery is the DiffuPT pipeline: a class-conditioned Latent Diffusion Model (LDM) trained on both glaucoma and non-glaucoma fundus images in a compressed latent space, with a learnable class embedder providing conditioning; a DDIM sampler that generates new images; a baseline Swin Transformer classifier that filters the generated images, keeping those classified as the target class; and a two-stage schedule that pretrains a Swin Transformer on the balanced synthetic set, then fine-tunes on the real data. The LDM autoencoder is trained with LPIPS loss, and the pretraining distribution is chosen by ablation, with the 50-50 glaucoma/non-glaucoma split performing best. Filtering, the paper argues, removes noise from the generated set and improves the final harmonic mean from 91.5% to 92.59%.","core_discovery":"The central claim is that pretraining a classifier on a balanced, diffusion-generated, baseline-filtered synthetic dataset and then fine-tuning on the real imbalanced dataset yields a more sensitive and specific glaucoma detector than standard training, weighted cross-entropy, weighted sampling, generation-based augmentation, or multi-stage decoupling. The paper explains the gain through embedding geometry: balanced synthetic pretraining lowers variance but increases overlap between classes, and fine-tuning on real data reduces that overlap, giving the best of both. The strongest reported result is a harmonic mean of 92.59% and AUC of 98.02% on the GlaucomaEgy test set, with a 1.09-point improvement attributable to the filtering stage alone.","pith_inferences":["I suspect part of the gain is a generic regularization effect: any large, balanced, slightly noisy pretraining set may stabilize features, so a controlled comparison against real-image oversampling is needed to separate synthetic-content effects from balance effects.","The filter's threshold is a hidden hyperparameter; varying it likely trades precision against recall in the pretraining set, and the paper's single fixed filter leaves this trade-off unexplored.","The same pipeline could apply to other imbalanced medical imaging tasks, but the main risk is circularity: if the baseline classifier's false negatives are exactly the subtle cases the generator is asked to produce, the filter may systematically remove the most informative synthetic examples.","If the generated images were released, they could serve as a privacy-preserving proxy for rare disease classes, though the paper does not test this."],"forward_implications":["If DiffuPT works as reported, class imbalance in glaucoma screening can be mitigated without collecting more real minority-class images or adding manual annotations.","The filtering stage shows that a baseline classifier can act as a quality gate for synthetic medical images, and the observed 1.09-point harmonic-mean gain indicates that which generated samples survive matters.","The comparison across generative models indicates that diffusion-based generation outperforms the tested GAN-based image-to-image methods in both generation quality (FID, KID) and downstream classification, making the generative model choice consequential.","The method's success on both GlaucomaEgy and AIROGS suggests the pretraining approach transfers across datasets and across ethnic variation in fundus appearance.","The ablation of pretraining distributions shows that a balanced 50-50 synthetic set gives the best AUC before fine-tuning, implying class balance in the pretraining data is important for the downstream gain."],"supporting_citations":[{"why":"Supplies the Latent Diffusion Model used to generate synthetic fundus images.","marker":"[30]"},{"why":"Provides the Swin Transformer backbone that is pretrained and fine-tuned.","marker":"[26]"},{"why":"Provides the AIROGS dataset and its two-grader-plus-adjudicator grading protocol, used for external validation.","marker":"[5]"},{"why":"Foundational DDPM formulation on which the diffusion training and sampling are based.","marker":"[13]"},{"why":"Supplies classifier-free guidance used for class-conditioned sampling.","marker":"[14]"},{"why":"DDIM sampler used to generate images faster during the generation stage.","marker":"[36]"},{"why":"Multi-stage decoupling baseline that DiffuPT is compared against.","marker":"[17]"}],"fun_headline_variants":["Diffusion-generated fundus images sharpen glaucoma detection to 92.6%","Synthetic diffusion data counter class imbalance for glaucoma screening","Diffusion pretraining lifts glaucoma detection to 92.59% harmonic mean","Balanced synthetic data from diffusion enhance glaucoma diagnosis","Diffusion-based generation mitigates class imbalance in glaucoma"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes that synthetic glaucoma images that survive the baseline classifier's filter are an informative stand-in for real glaucoma images during pretraining; if the filter silently drops the hard, informative cases, the pretraining set could reinforce the baseline's blind spots rather than correct them.","fun_headline_variants_meta":{"raw":{"variants":["Diffusion-generated fundus images sharpen glaucoma detection to 92.6%","Synthetic diffusion data counter class imbalance for glaucoma screening","Diffusion pretraining lifts glaucoma detection to 92.59% harmonic mean","Balanced synthetic data from diffusion enhance glaucoma diagnosis","Diffusion-based generation mitigates class imbalance in glaucoma"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000975,"raw_usage":{"total_tokens":4127,"prompt_tokens":911,"completion_tokens":3216,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":527,"completion_tokens_details":{"reasoning_tokens":3131}},"tokens_in":527,"tokens_out":3216,"duration_ms":24358,"temperature":1.0,"reasoning_tokens":3131,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T22:19:54.685220+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test: pretrain the same Swin Transformer on a balanced set of real glaucoma images (e.g., oversampled or additionally collected) with identical compute, learning rate, and iteration counts. If real-balanced pretraining matches or exceeds DiffuPT's 92.59% harmonic mean, then the diffusion-generated content is not the active ingredient. A second check is to measure the filter's rejection rate on generated glaucoma images and inspect what it rejects; if it systematically removes subtle or early-stage cases, the mechanism is suspect.","supporting_citations":[{"cited_title":"Airogs: Artificial intelligence for robust glaucoma screening challenge","cited_arxiv_id":null,"evidence_quote":"Provides the AIROGS dataset and its two-grader-plus-adjudicator grading protocol, used for external validation."}],"review_version":1}