Pith. sign in

REVIEW 4 major objections 6 minor 27 references

Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read cUPMI, a class-conditional Gaussian augmentation of stacked log-probabilities, consistently regularizes high-capacity tree combiners in IPMN risk stratification.

desk verdict Worth refereeing, but the "positive in all seeds" cUPMI claim needs a proper seed-level test before it should be believed. read the letter →

arxiv 2608.11472 v1 pith:P3EALI7P submitted 2026-08-11 cs.CV cs.LG

Max A. Nelson , Eminenur Sen Tasci , Zhixiang Wang , Zongwei Zhou , Halil Ertugrul Aktas , Andrea M. Bejar , Elif Keles , Ziliang Hong
show 7 more authors
S{i}tk{i} Safa Taflan Muhammed Enes Tasci Frank H. Miller Michael B. Wallace Rajesh N. Keswani Gorkem Durak Ulas Bagci
This is my paper · ORCID
classification cs.CVcs.LG
keywords ensemblestackingclass-conditionalGaussianaugmentationordinalriskstratificationIPMNdysplasiamultimodalMRIpancreaticcancerregularizationfold-lockedfusion
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

Pancreatic cancer risk stratification of IPMNs from MRI relies on fusing many weak signals, and the paper proposes cUPMI, a regularizer that fits a class-conditional Gaussian to the log-probability outputs of base classifiers and injects synthetic samples before a stacking combiner. Its central claim is that this augmentation consistently improves random forest and XGBoost stacking in small-cohort settings, with gains of +0.015 and +0.024 binary AUC and +0.022 QWK for XGBoost on an 8-stream radiomics task, positive in all seeds, while leaving already-regularized logistic stacks unchanged. The paper also shows that fold-locked fusion of radiomics and 2.5D CNN streams yields the strongest overall model, an RF stack reaching QWK 0.595 and AUC 0.839, surpassing single-architecture baselines. If right, cUPMI is a simple, capacity-specific regularizer for tree-based ensemble fusion in medical imaging.

What carries the argument

The central object is the class-conditional Gaussian sampler defined in Equation (2): on the training fold, cUPMI fits a Gaussian with class-specific mean and a shared pooled covariance plus a ridge term of $10^{-4}$ over the stacked log-probability vectors of dimension $3S$, then draws a number of balanced synthetic samples, controlled by a ratio selected via inner cross-validation, and appends them to the level-1 combiner's training set. This mechanism smooths the decision surface of high-capacity tree combiners without adding hand-engineered interaction features, and it deliberately uses a single shared component rather than a BIC-selected within-class mixture, which the paper found unreliable at this sample size.

What would settle it

One concrete check is to estimate the actual per-class meta-feature distributions from the out-of-fold log-probabilities, for example with a multivariate normality test such as Mardia's, or by comparing the fitted Gaussian density to a kernel density estimate, and then test whether cUPMI's gain disappears on cohorts where that assumption fails. Another is an ablation that replaces cUPMI's samples with bootstrap resamples of the real meta-features; if the bootstrap performs equally or better, the Gaussian parametric form is not the source of the regularization benefit.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that cUPMI, which draws synthetic samples from one Gaussian per class with a shared pooled covariance over stacked log-probability meta-features, regularizes high-capacity tree combiners in a way that L2-logistic stacks do not need. In binary whole-organ radiomics, it raises RF AUC by +0.015 and XGBoost by +0.024 with positive gains in all seeds; in the ordinal three-class task, its cleanest benefit is XGBoost on the eight-stream radiomics setting, +0.022 QWK in all seeds. Separately, the strongest overall discrimination comes from fold-locked fusion of radiomics and deep-learning streams under an RF stack, achieving QWK 0.595 with a 95% confidence interval of [0.54, 0.64] and binary AUC 0.839, outperforming radiomics, a 2.5D CNN, and a 3D DenseNet-121. The effect scales with combiner capacity: the regularized linear stack gains essentially nothing, tree combiners gain consistently, and cUPMI does not help once cross-track fusion already lifts the tree combiners. The paper frames the contribution as one of fusion effectiveness and combiner regularization rather than a new discriminative architecture.

Load-bearing premise

The load-bearing premise is that the stacked log-probability meta-features follow a roughly Gaussian distribution per class with a shared pooled covariance; if they are multimodal, heavy-tailed, or class-dependent in covariance, the synthetic samples drawn from this fitted Gaussian will be unrealistic and could hurt rather than help the combiner.

Editorial extensions

If this is right

  • cUPMI offers a cheap, seed-reproducible regularizer for tree-based stacking in small-cohort medical imaging, with gains concentrated on high-capacity combiners.
  • L2-logistic stacks need no such augmentation, so the benefit is capacity-specific rather than universal.
  • Fold-locked fusion of radiomics and 2.5D CNN streams, with anatomical sub-regions, outperforms a heavier 3D DenseNet-121, suggesting that complementary signal fusion can beat architectural scale on small ordinal cohorts.
  • The strongest model's errors concentrate on adjacent ordinal grades with near-zero two-step confusions, showing the ordinal target is respected by the RF stack.
  • Per-combiner QWK gains are small relative to patient-sampling variability, with 95% confidence intervals including zero, so practical benefit should be judged by reproducibility across seeds.

Reading between the lines

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

  • Extending beyond the paper: if the Gaussian meta-space assumption holds beyond IPMN, cUPMI could be applied to any stacking pipeline whose base learners output class probabilities, for example multimodal tabular or sensor fusion where tree combiners overfit.
  • A reader could test whether the class-conditional structure matters by comparing cUPMI against plain Gaussian or uniform noise injection on the same meta-features; if noise alone reproduces the gain, the fitted mean and covariance are not the operative part.
  • Because the paper notes BIC-selected mixtures were unreliable, a testable extension is a regularized mixture with sparse or prior-driven component selection, which might recover some lost flexibility at larger sample sizes.
  • The fold-locked fusion result suggests a broader design rule: train base learners once under a fixed split and only then choose the combiner and its regularization, so that combiner selection does not leak across folds.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes cUPMI, a regularizer for level-1 stacking combiners in multimodal IPMN risk stratification. cUPMI fits one class-conditional Gaussian per class with a shared pooled covariance to log-probability meta-features (Eq. 2), draws synthetic samples, and appends them to the training fold. The authors evaluate binary AUC and ordinal QWK across radiomics, 2.5D ResNet, 3D DenseNet, and fused settings under repeated stratified 5-fold cross-validation with fold-locked fusion. They report that cUPMI consistently regularizes higher-capacity tree combiners in binary and radiomics-only settings (RF +0.015 and XGBoost +0.024 binary AUC, positive in all seeds; XGBoost radiomics-only +0.022 QWK in all seeds), while adding limited or negative value to L2-logistic stacks and to the fused DL+radiomics RF stack. The strongest overall model is a fold-locked RF stack over fused DL+radiomics without cUPMI (QWK 0.595, 95% CI [0.54, 0.64]; binary AUC 0.839).

Significance. If the consistency claim held, cUPMI would be a simple, inexpensive regularizer for tree-based stacking in small-cohort medical imaging, with modest but reproducible gains. The paper's strengths are its leakage-controlled nested protocol, repeated-seed reporting, patient-level bootstrap CIs, and honest discussion of limitations. The method is distinct from SMOTE and mixup and is easy to implement. However, the effect sizes are small, the 'all seeds' language rests on very few seeds, and the statistical support is weaker than the presentation suggests. The contribution is therefore promising but not yet established at the level claimed.

major comments (4)
  1. [§4, Table 3; Abstract] The phrase 'positive in all seeds' is load-bearing, but the protocol states only 'five or more seeds' and Table 3 reports only the percentage of improved seeds, not seed counts or individual deltas. With five seeds and a true zero effect, the probability of all five deltas being positive is 1/32 and the probability of all five having the same sign is 1/16. Table 3 alone contains six combiner/setting comparisons, so the chance of at least one all-positive pattern by chance is about 17%, and the chance of at least one all-same-sign pattern is about 32%; the binary comparisons add two more configurations. The consistency claim must be supported by a seed-level paired test (e.g., paired Wilcoxon or permutation test on the per-seed deltas) and by reporting the exact number of seeds and all per-seed deltas for every reported configuration.
  2. [Table 3, §5] The Δ columns in Table 3 report only a point delta and the fraction of seeds improved; the associated 95% confidence intervals are not shown. The authors themselves state in §5 that 'per-combiner ΔQWK remains small relative to patient-sampling variability (95% CIs include zero),' so the reader cannot tell whether the headline +0.022 QWK gain is distinguishable from sampling noise. Report confidence intervals for every delta, ideally from the same patient-level bootstrap used for the stack columns, and make the seed-level results available.
  3. [§3.2, Eq. (2)] The entire cUPMI intervention consists of sampling synthetic meta-features from a single shared-covariance Gaussian in log-probability space, yet the paper provides no validation of this distributional assumption on the actual meta-features. The only evidence offered is the sentence in §3.2 that a BIC-selected mixture reduced the XGBoost gain from +0.022 to +0.004, with no diagnostics, no seed counts, and no confidence intervals. Because the synthetic samples are the method, the Gaussian assumption is load-bearing: please provide goodness-of-fit checks (e.g., tests of multivariate normality, per-class covariance comparisons, or Q-Q plots) or compare against a nonparametric alternative (e.g., smoothed bootstrap resampling of training meta-features) that does not depend on the Gaussian model.
  4. [§4, Table 3; Abstract] The paper's strongest overall model (RF stack over fused DL+radiomics, QWK 0.595) is achieved without cUPMI, and in the S8+DL fused setting cUPMI slightly hurts RF (-0.003) and LR (-0.007) while only marginally helping XGBoost (+0.005, 80% of seeds). The abstract and conclusion should therefore state more precisely that the consistent benefit is limited to specific combiner and stream configurations, rather than implying a generally consistent regularization of tree combiners. The current phrasing overreaches relative to the evidence presented.
minor comments (6)
  1. [References, [16]] The bibliography entry for [16] contains an internal note to 'confirm which title + full author list before submission'; this must be resolved and removed before publication.
  2. [Table 3] The column labels 'S8' and 'S8+DL' are not defined before the table; define them (e.g., 8 radiomics streams versus 8 radiomics plus 8 deep-learning streams) in the caption or in §3.
  3. [§3.3] The evaluation section says 'repeated stratified 5-fold cross-validation (on five or more seeds)' without specifying the exact number of seeds per configuration; report the seed count for each row in the tables.
  4. [Figure 3(d)] The caption states 'PC1 ≈ ordinal risk axis' without explaining how the PCA was computed or how this claim was assessed; add a sentence describing the PCA input and the variance decomposition.
  5. [§3.2] The statement 'Previously tested configurations include per-class and BIC-selected multi-component mixtures as well as full versus diagonal covariance' reports results that are not shown; either include these results or clearly mark them as preliminary and omit quantitative claims such as the +0.004 (40%) figure.
  6. [§3.3] The sentence 'Full multiclass calibration remains planned, though notably does not underpin the QWK or macro-AUC results' is vague about whether any calibration was applied to the ordinal outputs; clarify the calibration status for the 3-class models.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: cUPMI is an empirical augmentation heuristic whose hyperparameters are selected by inner cross-validation and whose reported effects are measured on held-out folds.

full rationale

The paper makes no claim that a target result is derived from its own definition. cUPMI's Gaussian fit (Eq. 2) estimates class-conditional means and a shared pooled covariance from training meta-features only; the synthesis ratio rho is selected by inner cross-validation within the training fold, and all reported QWK and AUC values are computed on outer folds. This is standard fitting of a generative augmentation model, not a fitted parameter renamed as a prediction. The central performance claims, such as XGBoost +0.022 QWK on the 8-stream radiomics task and the RF stack QWK 0.595, are measured outcomes rather than consequences of the Gaussian assumption by construction. The paper itself flags the relevant limitations: per-combiner delta-QWK has 95% confidence intervals including zero, and the minority-class sample size approaches a regime where the sampler becomes unreliable; these weaken the strength of the consistent-regularization claim but are statistical-evidence issues, not circularity. Citations to prior work from the same group, including the Cyst-X dataset and earlier radiomics-boosting work, are used as a data source and as baseline comparisons, not as an unverified premise that forces the conclusion. No self-definitional reduction, fitted-input-as-prediction step, or load-bearing self-citation chain is present. Therefore the derivation is self-contained with respect to circularity, and the honest finding is a score of 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim that cUPMI regularizes tree combiners rests on the Gaussian model of meta-features, on the assumption that synthetic samples from that model help generalization, and on a leakage-controlled evaluation split. These are domain assumptions, not mathematical axioms.

free parameters (1)
  • synthesis ratio rho = Not reported
    Controls how many synthetic meta-features are generated; selected via inner cross-validation within each training fold, so it is data-driven and affects the cUPMI results.
assumptions (3)
  • domain assumption Meta-features in log-probability space are approximately Gaussian per class with a shared pooled covariance
    Stateed in Section 3.2, Eq. (2). The synthetic samples that define cUPMI are drawn from this Gaussian, so the method's benefit depends on this distributional assumption being reasonable.
  • domain assumption Synthetic samples from the fitted Gaussian improve generalization of the level-1 combiner
    The entire cUPMI rationale is that enriching the training set with class-conditional Gaussian samples smooths the combiner's decision surface; this is an empirical assumption tested only indirectly.
  • domain assumption Log-probability clipping at epsilon = 1e-6 does not materially distort the distribution
    Applied component-wise in Eq. (1); necessary for numerical stability, but the clipping changes the distribution near probability 0 or 1, which could affect the Gaussian fit.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification." pith.science (2026). https://pith.science/paper/P3EALI7P

@misc{pith2026260811472,
  author       = {Pith},
  title        = {Pith review of: Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P3EALI7P}},
  note         = {Machine review of arXiv:2608.11472}
}
read the original abstract

Pancreatic cancer is among the most lethal malignancies; risk stratification of intraductal papillary mucinous neoplasms (IPMNs) offers a crucial opportunity for early intervention but typically requires invasive tissue biopsy. Dominant vision-based approaches, including radiomics and deep learning, provide promising but initially separate discrimination opportunities. Similarly, multisequence MRI (T1W/T2W) and anatomically decomposed (head, body and tail) analysis of the pancreas provide additional and potentially complementary signals. Effective fusion of this information is crucial in ordinal IPMN dysplasia risk prediction and can be accomplished via a meticulously regularized and calibrated ensemble stacking combiner. We present cUPMI, a class-conditional Gaussian augmentation of a combiner's log-probability meta-features, and test it on various prediction paradigms. In our multi-center analysis, we find cUPMI adds limited value to properly regularized L2-logistic binary classification stacks, but consistently regularizes higher-capacity tree combiners in the binary and radiomics-only setting (RF +0.015 and XGBoost +0.024 binary AUC, positive in all seeds). Its cleanest ordinal benefit appears for XGBoost on an 8-stream radiomics task (3-class no < low < high, +0.022 QWK in all seeds). Separately, fold-locked fusion of radiomics and 2.5D CNN streams yields the strongest overall model, an RF stack reaching QWK 0.595 (95% CI [0.54, 0.64]) and binary AUC 0.839, surpassing radiomics, 2.5D ResNet, and 3D DenseNet-121 baselines.

Figures

Figures reproduced from arXiv: 2608.11472 by the authors.

Figure 1
Figure 1. Stacked-fusion pipeline. Radiomics and CNN base learners produce per-stream probabilities, concatenated fold-locked into stacked log-probability meta-features. cUPMI augments the training fold with class-conditional Gaussian synthetic meta￾features upstream of a high-capacity level-1 combiner. 1 Introduction Intraductal papillary mucinous neoplasms (IPMNs) are common incidental find￾ings on imaging [2] and are detec… view at source ↗
Figure 2
Figure 2. Visualization of representative cases and segmentations. (a) Whole organ pan￾creas segmentation on T1W and T2W MRI. (b) Subregional pancreas segmentation on T2W MRI (head/body/tail). (c) Cases corresponding to binary (low/high) labels on T1W and T2W MRI, boxes indicate cyst location [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Evaluation of the strongest full-fusion model (RF over fused DL+radiomics; 3-class, n=616). (a) One-vs-rest ROC with per-class and macro AUC. (b) Row￾normalized confusion: errors concentrate on the adjacent grade, with the costly two￾step no↔high confusions near zero (0.02/0.04). (c) Reliability of the predicted high-risk probability. (d) PCA of the model’s 3-class log-probability outputs (centroids ×): the ordinal … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

27 extracted references · 22 canonical work pages

  1. [1]

    Random forests.Machine Learning, 45(1):5–32, 2001

    Leo Breiman. Random forests.Machine Learning, 45(1):5–32, 2001

    2001

  2. [2]

    Incidental pancreatic cystic neoplasms in an asymptomatic healthy population of 21,745 individuals: Large-scale, single-center cohort study

    Ye Rim Chang, Joo Kyung Park, Jin-Young Jang, Wooil Kwon, Jeong Hee Yoon, and Sun-Whe Kim. Incidental pancreatic cystic neoplasms in an asymptomatic healthy population of 21,745 individuals: Large-scale, single-center cohort study. Medicine (Baltimore), 95(51):e5535, 2016

    work page 2016

  3. [3]

    SMOTE: Synthetic minority over-sampling technique.Journal of Artificial Intel- ligence Research, 16:321–357, 2002

    NiteshV.Chawla,KevinW.Bowyer,LawrenceO.Hall,andW.PhilipKegelmeyer. SMOTE: Synthetic minority over-sampling technique.Journal of Artificial Intel- ligence Research, 16:321–357, 2002

    work page 2002

  4. [4]

    XGBoost: A scalable tree boosting system

    Tianqi Chen and Carlos Guestrin. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794. ACM, 2016

    work page 2016

  5. [5]

    Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit.Psychological Bulletin, 70(4):213–220, 1968

    Jacob Cohen. Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit.Psychological Bulletin, 70(4):213–220, 1968

    1968

  6. [6]

    Nehra, Mark G

    Jaime de la Fuente, Arjun Chatterjee, Jacob Lui, Anand K. Nehra, Mark G. Bell, Robert J. Lennon, Shounak Majumder, et al. Long-term outcomes and risk of pancreatic cancer in intraductal papillary mucinous neoplasms.JAMA Network Open, 6(10):e2337799, 2023

    work page 2023

  7. [7]

    Besselink, Linde Scholten, Marco J

    Marco Del Chiaro, Marc G. Besselink, Linde Scholten, Marco J. Bruno, D. N. Cahen, Thomas M. Gress, Jeanin E. van Hooft, et al. European evidence-based guidelines on pancreatic cystic neoplasms.Gut, 67(5):789–804, 2018

    work page 2018

  8. [8]

    ImageNet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. InIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 248–255, 2009

    2009

Show all 27 references
  1. [9]

    Elta, Brintha K

    Grace H. Elta, Brintha K. Enestvedt, Bryan G. Sauer, and Anne Marie Lennon. Acg clinical guideline: Diagnosis and management of pancreatic cysts.The Amer- ican Journal of Gastroenterology, 113(4):464–479, 2018

    2018

  2. [10]

    Weinberger

    Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. InProceedings of the 34th International Conference on Machine Learning (ICML), volume 70 ofProceedings of Machine Learning Re- search, pages 1321–1330. PMLR, 2017

    2017

  3. [11]

    Deep residual learn- ing for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learn- ing for image recognition. InIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016

    2016

  4. [12]

    Weinberger

    Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. InIEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 4700–4708, 2017

    2017

  5. [13]

    Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi, Imon Banerjee, and Matthew P. Lungren. Fusion of medical imaging and electronic health records using deep learn- ing: a systematic review and implementation guidelines.npj Digital Medicine, 3(1):136, 2020. 10 M. A. Nelson et al

    2020

  6. [14]

    Miller, Camila Lopes Vendrami, Hannah S

    Frank H. Miller, Camila Lopes Vendrami, Hannah S. Recht, Cecil G. Wood, Pardeep Mittal, Rajesh N. Keswani, Helena Gabriel, Amir A. Borhani, Paul Niko- laidis, and Nancy A. Hammond. Pancreatic cystic lesions and malignancy: Assess- ment, guidelines, and the field defect.Radiogr...

    2022

  7. [15]

    Lennon, et al

    Tetsuhide Ohtsuka, Carlos Fernández-del Castillo, Tatsuo Furukawa, Susumu Hi- jioka, Jin-Young Jang, Anne M. Lennon, et al. International evidence-based Kyoto guidelines for the management of intraductal papillary mucinous neoplasm of the pancreas.Pancreatology, 24(2):255–270, 2024

    2024

  8. [16]

    A Federated AI System Out- performs Clinical Guidelines to Detect Pancreatic Cancer Precursors

    Hongyi Pan, Gorkem Durak, Elif Keles, Ziliang Hong, Deniz Seyithanoglu, et al. Cyst-x: A multi-center MRI benchmark and federated learning framework for malignancy-risk stratification of pancreatic cystic neoplasm.arXiv preprint arXiv:2507.22017, 2025. arXiv listing title diff...

    2025 arXiv

  9. [17]

    Enrique Pérez-Cuadrado Robles, Frédérique Maire, Jérôme Cros, Marie-Pierre Vul- lierme, Vinciane Rebours, Alain Sauvanet, et al. Accuracy of 2012 international consensus guidelines for the prediction of malignancy of branch-duct intraductal papillary mucinous neoplasms of the ...

    2012

  10. [18]

    John C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Alexander J. Smola, Peter Bartlett, Bernhard Schölkopf, and Dale Schuurmans, editors,Advances in Large Margin Classifiers, pages 61–74. MIT Press, 1999

    1999

  11. [19]

    Baril, Michele T

    Muhammad Ibtsaam Qadir, Jackson A. Baril, Michele T. Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C. Max Schmidt, and Fiona R. Kolbinger. Arti- ficial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.PLOS Digital Health, 4(...

    2025

  12. [20]

    Alexander K. Seewald. How to make stacking better and faster while also taking care of an unknown weakness. In Claude Sammut and Achim G. Hoffmann, edi- tors,Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pages 554–561, Sydney, Australi...

    2002

  13. [21]

    Multi- modal deep learning for biomedical data fusion: a review.Briefings in Bioinfor- matics, 23(2):bbab569, 2022

    Sören Richard Stahlschmidt, Benjamin Ulfenborg, and Jane Synnergren. Multi- modal deep learning for biomedical data fusion: a review.Briefings in Bioinfor- matics, 23(2):bbab569, 2022

    2022

  14. [22]

    Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas.Pancreatol- ogy, 17(5):738–753, 2017

    Masao Tanaka, Carlos Fernández-del Castillo, Terumi Kamisawa, Jin-Young Jang, Philippe Levy, Tetsuhide Ohtsuka, et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas.Pancreatol- ogy, 17(5):738–753, 2017

    2017

  15. [23]

    Kai Ming Ting and Ian H. Witten. Stacked generalization: when does it work? InProceedings of the Fifteenth International Joint Conference on Artificial Intel- ligence (IJCAI-97), pages 866–871. Morgan Kaufmann, 1997

    1997

  16. [24]

    Vanden Bulcke, J

    A. Vanden Bulcke, J. Jaekers, H. Topal, D. Vanbeckevoort, V. Vandecaveye, T. Roskams, et al. Evaluating the accuracy of three international guidelines in identifying the risk of malignancy in pancreatic cysts: a retrospective analysis of a surgical treated population.Acta Gast...

    2021

  17. [25]

    David H. Wolpert. Stacked generalization.Neural Networks, 5(2):241–259, 1992

    1992

  18. [26]

    Radiomics boosts deep learning model for IPMN classifi- cation

    Lanhong Yao, Zheyuan Zhang, Ugur Demir, Elif Keles, Camila Vendrami, Emil Agarunov,CandiceBolan,IvoSchoots,MarcBruno,RajeshKeswani,FrankMiller, Augmenting Stacking Ensembles for IPMN Risk 11 Tamas Gonda, Cemal Yazici, Temel Tirkes, Michael Wallace, Concetto Spamp- inato, and U...

    2023

  19. [27]

    Dauphin, and David Lopez-Paz

    Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. InInternational Conference on Learning Rep- resentations (ICLR), 2018

    2018

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.