{"id":"4c3b0f04-374e-4e57-a96a-8fafc8619ae8","arxiv_id":"2506.03177","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A deep learning mammography system trained and validated on Thai hospital data achieves AUROCs of 0.89-0.96 across in-domain, biopsy-confirmed, and out-of-domain datasets, with high radiologist acceptance.","lead":"This paper describes a deep learning system for breast cancer detection in mammograms, trained on Thai hospital data and validated on three separate datasets. It reports high accuracy and strong acceptance by radiologists, suggesting the system could support breast cancer screening in Thailand.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The generalizability claim rests on the 761-case out-of-domain set, but the paper never states how its reference standard was established; if those labels are unverified radiology reports rather than pathology or follow-up, the 0.937 AUROC may not measure true cancer detection.","rationale":"The reader correctly identified reference-standard risk for the in-domain set, but the more load-bearing version is the external generalization set, because it is the only truly multi-center evidence and its reference standard is entirely unspecified. The biopsy-confirmed set, with pathology and two-year follow-up, provides credible single-center support; the in-domain set is weakened by shared report-based labels and possible patient-level leakage, but it is not the pillar of the generalizability claim. My concern does not overturn the reader's CONDITIONAL verdict, because the external set's small size and unverified labels warrant the same request for clarification and additional validation. The concrete test is feasible if follow-up or pathology data can be retrieved; if it cannot, that absence itself should be reported as a limitation.","tokens_in":12346,"tokens_out":6975,"duration_ms":75227,"concrete_test":"Re-adjudicate all 761 generalization cases against a verified truth standard: use pathology results where available and two-year follow-up for the remainder, with two radiologists blinded to the original report and to model output, and recompute the AUROC from Table 9. If the verified-truth AUROC is not significantly lower than 0.937, the concern is refuted; if it drops materially, the claimed multi-center generalizability is not established.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that the model detects breast cancer accurately across independent multi-center data, with AUROC 0.937 on the generalization set (Table 9). The most load-bearing condition is therefore that the out-of-domain labels reflect true cancer status. The paper explicitly verifies reference standards only for the biopsy-confirmed set (Section 4.2: pathology plus two-year follow-up for benign/normal). For the generalization set (Section 4.3), no reference-standard construction is described; Table 3 lists BIRADS and abnormality categories but not how those labels were obtained. If the 761 external cases are labeled from original radiology reports without independent adjudication or outcome follow-up, then the model is being scored against the same kind of subjective report labels used in training, just from different hospitals. This can inflate AUROC through shared reporting conventions, particularly at the benign-versus-suspicious boundary, and it cannot separate true cancer status from radiologist style. The in-domain set (Section 3.2) has the same limitation and comes from the training institution, so the external set is the only true multi-center evidence. Biopsy-confirmed results are strong but single-center. Thus the weakest load-bearing premise is the unverified reference standard for the 761 cases that carry the generalizability claim.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript describes a deep learning system (Inspectra MMG) for breast cancer detection in mammography, trained on 58,022 mammograms from Siriraj Hospital and evaluated on three validation sets: an in-domain held-out set (9,421 cases), a biopsy-confirmed set (883 cases), and an out-of-domain set (761 cases from two other hospitals). The authors report AUROC values of 0.894, 0.963, and 0.937 on these sets, along with lesion-localization metrics (LLF/NLF), concordance rates with radiologist reports (83.5% and 78.1% classification), radiologist acceptance rates (96.7% and 89.3%), and System Usability Scale scores (74.17 and 69.20). The paper claims that the system is a reliable AI assistant for mammogram interpretation in Thai clinical settings.","tokens_in":12614,"tokens_out":9089,"duration_ms":88049,"significance":"If the results are accurate, this is a valuable contribution to the literature on AI in mammography, providing the first large-scale validation for a Thai population and including a multi-center out-of-domain set and a biopsy-confirmed reference set. The biopsy-confirmed set with pathology and two-year follow-up is a particular strength, and the evaluation of localization and clinical usability goes beyond simple classification metrics. However, several methodological issues—most importantly the unverified reference standard for the out-of-domain set and the partly tautological acceptance-rate calculation—need to be resolved before the generalizability and clinical-acceptance claims can be accepted.","major_comments":[{"comment":"The reference standard for the 761-case generalization set is not described. The paper does not state whether the labels come from pathology, follow-up, adjudicated expert review, or original radiology reports; Table 3 reports BIRADS and abnormality categories but not label provenance. Since the out-of-domain AUROC (0.937) is the primary evidence for multi-center generalizability, this omission is critical. If the labels are derived from unverified radiology reports, the result may reflect agreement with reporting style rather than true cancer status. Please specify the labeling process, any independent verification, and support it with outcome data if available.","section":"Section 4.3, Table 3"},{"comment":"The use of the Clopper-Pearson method for AUC confidence intervals is statistically invalid. Clopper-Pearson is an exact interval for binomial proportions; the AUC is a rank-based statistic and its sampling distribution is not binomial. The reported AUC intervals in Tables 5, 7, and 9 should be recomputed with a rank-based method (e.g., DeLong's test or bootstrap), and the methods section corrected accordingly. The Clopper-Pearson intervals for sensitivity and specificity may be retained, but the sentence 'Clopper-Pearson method was used for estimating 95% CIs for AUC' is incorrect as written.","section":"Section 3.3, Tables 5/7/9"},{"comment":"The radiologist acceptance rate is inflated by automatically counting all 'Agree' concordance cases as accepted (423 of 883 and 307 of 761). Because 'Agree' is defined in Table 11 as 'Radiologist report completely matches with AI,' these cases are accepted by definition whenever the AI agrees with the report, making the resulting 96.7% and 89.3% acceptance rates partly tautological. Please report the acceptance rate based on radiologist review of all cases, or at minimum provide the acceptance rate for the manually reviewed subset separately from the automatically accepted subset.","section":"Section 5.2, Tables 15/16"},{"comment":"The concordance definition counts 'Add' cases as concordant. An 'Add' case is one where the radiology report contains findings not detected by the AI (Table 11) or where the AI heatmap misses lesions (Table 12); treating these as concordant means that missed lesions are counted as agreement. This inflates the concordance rates, particularly for the generalization set, where 'Add' comprises 7.2% (classification) and 6.8% (localization) of cases. Report 'Agree,' 'Edit,' 'Add,' and 'Reject' separately, and do not pool 'Add' with true agreement unless a clear clinical justification is provided.","section":"Section 5.1, Tables 11/12/13/14"},{"comment":"The sentence 'All values are computed based on the optimal operating point of the model' is ambiguous but suggests that the operating point (threshold) may have been selected using the test set. If so, the reported sensitivity and specificity are optimistically biased. Please state how the threshold was chosen (e.g., fixed a priori, selected on the validation split only) and, if it was optimized on the test set, re-estimate sensitivity and specificity using a pre-specified threshold.","section":"Section 4.1"}],"minor_comments":[{"comment":"The sum of the 'Condition' categories is 70,504, which exceeds the reported dataset size of 58,022; clarify whether cases can have multiple conditions and provide mutually exclusive counts or explain the overlap.","section":"Section 3.1, Table 1"},{"comment":"The in-domain test set is described as containing 9,421 mammograms, but a 15% split of 58,022 would be approximately 8,703; clarify whether the 58,022 figure refers to images and the 9,421 to examinations or vice versa.","section":"Section 4.1"},{"comment":"The total lesion counts for the 'Cancer' row do not match the sum of the per-type rows (e.g., in Table 6, 1,879 vs. 1,917); reconcile these numbers or state that 'Cancer' includes only a subset of lesions.","section":"Tables 6/8/10"},{"comment":"Treating a 'neutral' score of 2 as acceptable may overstate acceptance; consider reporting the full distribution of scores (1-4) and the percentage rated 3 or 4.","section":"Section 5.2"},{"comment":"The reference standard for the development set is described as based on radiology reports; please state whether any pathology confirmation was available for a subset of the development data, and whether the same labeling protocol was applied to the in-domain test set.","section":"Section 3.2"}],"recommendation":"major_revision","confidential_remarks":"This manuscript is co-authored by employees of Perceptra, the company producing the system, and the validation includes datasets from the same institutions as the authors. The paper would benefit from a clear statement of the reference-standard verification for the generalization set and from a more critical analysis of the acceptance metric. The authors should also ensure that the claim of being the 'first large-scale validation study in Thai population' is accurate relative to the existing literature. Despite these issues, the biopsy-confirmed evaluation is a genuine strength, and the paper is likely salvageable with major revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's the short version: this is a legitimate and useful clinical validation study, not a technical breakthrough. The architecture is a modified EfficientNetV2 with ACM and external attention; that part is incremental. The value is in the evaluation: three datasets, including a biopsy-confirmed set with pathology and two-year follow-up, an out-of-domain set from two other hospitals, plus concordance and usability data from practicing radiologists. As far as I know this is the first large-scale multi-center validation of a mammography AI in a Thai population, and those numbers (AUROC 0.89 in-domain, 0.96 biopsy, 0.94 out-of-domain) are plausible.\n\nWhere it gets soft: the out-of-domain set is the load-bearing evidence for generalizability, and the paper never says how its reference standard was built. Section 4.3 just says the data came from a network of hospitals and lists BIRADS categories. If those labels are original radiology reports without independent adjudication, the 0.937 AUROC may reflect shared reporting conventions more than true cancer status. The in-domain set has the same limitation, and it shares the training institution's reports. The biopsy-confirmed set avoids that, but it is single-center. So the real external claim rests on 761 cases with an unstated label-generation process. That needs to be fixed before publication.\n\nThe statistics have two issues. Clopper-Pearson is for binomial proportions, not AUC; they should use DeLong or bootstrap. And the acceptance rate is partly tautological: cases where AI and radiologist agree are automatically counted as accepted, so 96.7% and 89.3% overstate the radiologists' independent judgment. The paper should report acceptance on the cases actually reviewed, with the auto-accepted cases shown separately.\n\nThe citation pattern is fair. No code or data is provided, which limits reproducibility, but that is common for clinical AI papers and not fatal. The benign-probability cap of 15% and the operating-point threshold are free parameters; a sensitivity analysis around them would help.\n\nOverall: the core claim holds up in its broad strokes, but the weakest link is real and identified. This paper deserves a serious referee. I would send it out, with a request to clarify the out-of-domain reference standard, fix the AUC CI method, and present the acceptance metric more honestly. I'd probably cite it as the Thai population benchmark, with caveats.","headline":"A credible, useful first large-scale multi-center validation of mammography AI in a Thai population, but the out-of-domain reference standard is unstated and the acceptance metric is partly self-referential.","tokens_in":13169,"tokens_out":2263,"would_cite":true,"duration_ms":23547,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that a deep learning system trained on mammograms from one Thai hospital detects breast cancer with AUROC 0.894 on its home test set, 0.963 on biopsy-confirmed cases, and 0.937 on cases from two other hospitals, and that…","keywords":["Breast cancer","Mammography","Screening","Deep learning","Artificial intelligence","Multi-center validation","Thai population","Lesion localization"],"falsifier":"Take a new cohort of screening mammograms from a hospital the model has never seen, determine cancer status only by biopsy or two-year follow-up, and compare the model's AUROC on that pathology-grounded cohort against the reported values; if it falls substantially below 0.937, the claim of generalizable, clinically reliable detection fails.","tokens_in":12214,"feed_emoji":"🩻","tokens_out":13620,"duration_ms":119443,"temperature":0.7,"pith_summary":"This paper claims that a mammography AI trained on images from one large Thai hospital can detect breast cancer in other Thai hospitals, not just at its training site. On the model's held-out home test set it reaches an AUROC of 0.894; on a biopsy-confirmed set, 0.963; and on 761 cases from two other hospitals, 0.937. The system also highlights suspicious lesions, with cancer-level localization fractions between about 0.71 and 0.86 depending on the dataset, and radiologists in the study accepted its outputs 96.7% and 89.3% of the time. The authors present these results as evidence that the system is a reliable AI assistant for mammogram interpretation in Thai clinical practice and that it generalizes to new hospital settings.","feed_headline":"Thai-trained mammogram AI scores 0.94 on new hospitals","feed_subtitle":"Trained at one Bangkok hospital, the AI reached AUROC 0.937 on outside data and experts accepted 89% of its findings.","key_machinery":"The load-bearing mechanism is the model's joint classification-and-localization architecture. The encoder is EfficientNetV2(S), but its default squeeze-and-excitation attention is replaced with an Attend-and-Compare Module, which compares each region of the image with its surrounding context in a way meant to mimic a radiologist comparing symmetric or related zones. An External Attention layer learns correlations across samples, and a decoder with skip connections enlarges the feature map to help find small scattered calcifications. Probabilistic-CAM pooling then aggregates the features into two simultaneous outputs: a flattened classification score and a spatial heatmap. Because the model is trained with both image-level labels and pixel-level lesion annotations, the same network learns to say whether cancer is present and where it is, which is what lets the paper evaluate both detection and localization.","core_discovery":"The authors claim that a single deep learning model can both classify and localize breast lesions in mammography for a Thai population. The architecture is a modified EfficientNetV2(S) encoder with an Attend-and-Compare attention module in place of the default attention, an external attention layer, a decoder with skip connections, and Probabilistic-CAM pooling that produces both a classification score and a heatmap. Trained on 58,022 mammograms from one large Thai medical center and validated on three sets—9,421 held-out in-domain cases (AUROC 0.894), 883 biopsy-confirmed cases (AUROC 0.963), and 761 out-of-domain cases from two other hospitals (AUROC 0.937)—the model detects calcification, mass, axillary adenopathy, and architectural distortion, and localizes them with lesion localization fractions from 0.461 (other lesions, out-of-domain) to 0.861 (cancer overall, biopsy-confirmed). Concordance with radiologist reports is 83.5% and 78.1% for classification and 84.0% and 79.6% for localization on the two clinical datasets, and radiologists accept the AI's results 96.7% and 89.3% of the time. The conclusion the paper draws is that the system is a dependable AI assistant for mammogram interpretation and generalizes across Thai hospital settings.","pith_inferences":["Inference: The in-domain AUROC of 0.894 is the figure most likely to be optimistic, because its reference standard is radiology reports from the same hospital where the model was trained; the strongest evidence of generalizability rests on just 761 out-of-domain cases, so a larger prospective cohort is the natural next test.","Inference: The paper does not stratify performance by breast density, and extremely dense breasts make up only about 5-6% of the validation sets; since dense tissue is common and makes cancer harder to see, a density-stratified analysis would be a meaningful extension.","Inference: The concordance definition counts 'edit' and 'add' as agreement, so the reported 78-84% agreement is not all-or-nothing matching; a stricter agreement metric would produce lower numbers and should be reported for clinical deployment decisions.","Inference: A practical extension the authors do not quantify is workflow economics: with specificity between 58% and 76% depending on the dataset, using the AI as a triage tool would generate a certain number of extra recalls or workups, and the net effect on radiologist workload depends on that false-positive rate."],"forward_implications":["If the reported out-of-domain AUROC of 0.937 holds in routine use, the system could act as a second reader in Thai screening programs, flagging cases that need a radiologist's closer look.","With sensitivity around 93% at the chosen operating point on out-of-domain data and acceptance rates of 96.7% and 89.3%, most AI findings would not require a change to the radiologist's read, meaning the main benefit is catching cases that might otherwise be missed.","Because the model produces heatmaps with lesion localization fractions of roughly 0.71 to 0.86 for cancer overall, it can direct the reader to the suspicious region instead of requiring a whole-image search.","Concordance rates of 78-84% between the AI and radiology reports imply that in about one in five cases the AI and human disagree, making the system better suited as a prompting tool for double-reading than as a standalone replacement."],"supporting_citations":[{"why":"Supplies the EfficientNetV2(S) encoder that the model modifies with new attention and pooling.","marker":"[11]"},{"why":"Provides the Probabilistic-CAM pooling that combines feature maps into classification and heatmap outputs.","marker":"[14]"},{"why":"Defines the Attend-and-Compare Module used to replace standard attention in the encoder.","marker":"[15]"},{"why":"Defines the lesion localization fraction and non-lesion localization fraction metrics used to score heatmaps.","marker":"[13]"},{"why":"Reports an earlier deep learning mammography system with AUROC 0.959, giving the performance context this study is compared against.","marker":"[9]"},{"why":"Shows combined AI plus radiologist reading reaching AUROC 0.942 on a large screening cohort, motivating the multi-center validation approach.","marker":"[10]"},{"why":"Supplies the concordance-rate method the paper adapts to measure agreement between radiologist reports and AI results.","marker":"[17]"},{"why":"Provides the acceptance-evaluation approach used to score how often radiologists accept AI outputs.","marker":"[18]"},{"why":"Defines the System Usability Scale used for the usability survey.","marker":"[19]"}],"fun_headline_variants":["Mammogram AI hits 0.94 AUROC on new Thai hospitals","Thai-trained AI detects breast cancer on unseen data","Breast cancer AI shows high accuracy across hospital sites","AI mammography reads align with radiologists in Thai study"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The results depend on the assumption that the radiology-report labels used to define cancer in the main in-domain test set—written at the same hospital where the model was trained—are an accurate measure of true cancer status; if those labels reflect report style rather than actual disease, the reported AUROC would overstate real-world performance.","fun_headline_variants_meta":{"raw":{"variants":["Mammogram AI hits 0.94 AUROC on new Thai hospitals","Thai-trained AI detects breast cancer on unseen data","Breast cancer AI shows high accuracy across hospital sites","AI mammography reads align with radiologists in Thai study"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000607,"raw_usage":{"total_tokens":2915,"prompt_tokens":1120,"completion_tokens":1795,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":736,"completion_tokens_details":{"reasoning_tokens":1727}},"tokens_in":736,"tokens_out":1795,"duration_ms":13875,"temperature":1.0,"reasoning_tokens":1727,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T12:47:02.649672+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a new cohort of screening mammograms from a hospital the model has never seen, determine cancer status only by biopsy or two-year follow-up, and compare the model's AUROC on that pathology-grounded cohort against the reported values; if it falls substantially below 0.937, the claim of generalizable, clinically reliable detection fails.","supporting_citations":[{"cited_title":"In: Interna- tional Conference on Machine Learning, pp","cited_arxiv_id":null,"evidence_quote":"Supplies the EfficientNetV2(S) encoder that the model modifies with new attention and pooling."},{"cited_title":"Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling","cited_arxiv_id":"2005.14480","evidence_quote":"Provides the Probabilistic-CAM pooling that combines feature maps into classification and heatmap outputs."},{"cited_title":"In: European Conference on Computer Vision, pp","cited_arxiv_id":null,"evidence_quote":"Defines the Attend-and-Compare Module used to replace standard attention in the encoder."},{"cited_title":"Medical Physics 35(2), 435–445 (2008)","cited_arxiv_id":null,"evidence_quote":"Defines the lesion localization fraction and non-lesion localization fraction metrics used to score heatmaps."},{"cited_title":"The Lancet Digital Health 2(3), 138–148 (2020)","cited_arxiv_id":null,"evidence_quote":"Reports an earlier deep learning mammography system with AUROC 0.959, giving the performance context this study is compared against."},{"cited_title":"JAMA Network Open 3(3), 200265 (2020)","cited_arxiv_id":null,"evidence_quote":"Shows combined AI plus radiologist reading reaching AUROC 0.942 on a large screening cohort, motivating the multi-center validation approach."},{"cited_title":"PLoS One 17(2), 0264383 (2022)","cited_arxiv_id":null,"evidence_quote":"Supplies the concordance-rate method the paper adapts to measure agreement between radiologist reports and AI results."},{"cited_title":"NPJ Digital Medicine 4(1), 31 (2021)","cited_arxiv_id":null,"evidence_quote":"Provides the acceptance-evaluation approach used to score how often radiologists accept AI outputs."},{"cited_title":"Journal of Usability Studies 8(2), 29–40 (2013) 22","cited_arxiv_id":null,"evidence_quote":"Defines the System Usability Scale used for the usability survey."}],"review_version":1}