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REVIEW 4 major objections 5 minor 30 references

Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims a CNN-LSTM model with LIME explanations can classify good versus poor IVF embryos, reporting 97.7% accuracy after augmentation.

desk verdict The reported 97.7% accuracy is an artifact of testing on augmented copies of 20 real images, and the paper's own metrics are internally inconsistent, so the central clinical claim collapses. read the letter →

arxiv 2506.06680 v3 pith:JHHAQAID submitted 2025-06-07 cs.CV cs.LG

Radha Kodali , Venkata Rao Dhulipalla , Venkata Siva Kishor Tatavarty , Madhavi Nadakuditi , Bharadwaj Thiruveedhula , Suryanarayana Gunnam , Durga Prasad Bavirisetti , Gogulamudi Pradeep Reddy This is my paper
classification cs.CVcs.LG
keywords embryoselectionblastocystclassificationCNN-LSTMLIMEexplainableAIinvitrofertilizationdataaugmentationmedicalimage
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 tries to establish that an automated CNN-LSTM classifier, guided by LIME explanations, can grade IVF embryo images into good and poor embryos with clinically relevant accuracy. On the paper's own terms, it reports 90% accuracy before augmentation and 97.7% after, with visual explanations showing which image regions drive each decision. The motivation is that manual embryo grading is time-consuming and subjective, and existing deep learning graders lack transparency. If the claim holds, embryologists could receive both a prediction and a visual rationale during embryo selection.

What carries the argument

The load-bearing mechanism is the CNN-LSTM pipeline: a 13-layer CNN with batch normalization and pooling extracts spatial features from whole embryo images, an LSTM with 128 hidden units captures sequential dependencies across the feature sequence, and a softmax layer outputs the good/poor decision. Interpretability is carried by LIME, which fits a locally weighted linear model on superpixel perturbations to produce a binary map of the regions that drove each classification. The authors also use rotation (-10° to +10°, probability 0.5) and reflection (probability 1) augmentation to inflate the 98-image dataset.

What would settle it

Train the model exactly as described, then run it on a fresh set of real blastocyst images from a different clinic that were not involved in training or augmentation; if the accuracy on those images approaches chance instead of roughly 97.7%, the generalization claim is refuted.

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Extended reading notes

Core claim

The central claim is that a CNN-LSTM model trained on a public embryo-image dataset expanded by rotation and reflection from 98 images to 1,470 images can classify good versus poor embryos and, with LIME, show the image regions behind each decision. The authors report a post-augmentation test accuracy of 97.7% ± 0.82% on 294 images, with good embryos at 100% and poor embryos at 95.5% accuracy, and a pre-augmentation accuracy of 90%. They further report a LIME fidelity score of 0.91 and a mean IoU of 0.74 between LIME-highlighted regions and expert-annotated embryo regions, which they take as evidence that the explanations are clinically relevant.

Load-bearing premise

The 97.7% accuracy claim assumes that a test set of 294 images produced by augmenting just 10 original test images per class represents how well the model would do on new, real embryo images.

Editorial extensions

If this is right

  • If the accuracy holds on real clinical images, embryologists could use the model as a second reader that flags good and poor embryos without manual grading.
  • The LIME maps give a concrete visual audit trail, potentially making an AI embryo grading decision reviewable by a clinician before transfer.
  • The reported per-class results imply a specific failure mode: poor embryos can be mislabeled as good (five misclassifications), which in practice would risk selecting a non-viable embryo.
  • The authors' stated next step—multi-class grading of blastocoel expansion stages—follows directly if the binary good/poor decision is reliable.

Reading between the lines

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

  • Beyond the paper, the test set construction is a real limitation: 294 test images are derived from only 10 original test images per class, so the reported 97.7% measures the model's invariance to rotation and reflection of a few embryos, not its ability to generalize to unseen embryos. An external validation set would be needed to support a clinical claim.
  • A testable extension would be to compare LIME's highlighted regions against embryologist annotations at the cell-structure level (inner cell mass versus trophectoderm), rather than a whole-embryo bounding IoU, to see whether the explanation aligns with the Gardner grading criteria.
  • Because LIME is model-agnostic, the same explanation pipeline could be grafted onto any future embryo-grading network; the paper's contribution would then be the interpretability workflow rather than the specific accuracy figure.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a CNN-LSTM binary classifier for blastocyst images (good vs. poor embryo) trained on a 98-image dataset expanded by rotation and reflection augmentation to 1,470 images, and it uses LIME for local interpretability. The authors report 90% accuracy before augmentation and 97.7% after augmentation, together with precision, recall, F1-score, and sensitivity metrics, and they claim the approach has potential for clinical application.

Significance. If validated on a genuinely independent test set, this work would offer a practical, interpretable embryo-grading pipeline for small IVF datasets, and the public availability of code and the use of LIME are useful steps. However, the central result is measured on an augmented test set derived from only 20 original images, and the reported metrics are internally inconsistent; these issues mean the main accuracy claim is not established. A new external validation study would be required before the clinical-potential claim can be assessed.

major comments (4)
  1. [§4, Table 1] The claimed 97.7% accuracy after augmentation is measured on a test set of 294 images that are generated from only 10 original test images per class via rotation (±10°) and reflection. This does not measure generalization to unseen embryos; it measures recognition of transformed copies of the same 20 blastocysts. The paper's own limitation statement in §4 ("synthetic augmentation cannot fully capture the complexity and diversity of real-world clinical data") concedes this point, but the abstract's "potential for clinical application" claim still rests on this invalid measure.
  2. [§4, Table 3 vs. Fig. 5] The confusion-matrix counts and the reported precision/recall values are mutually inconsistent. With 147 test images per class and five poor embryos misclassified as good (Fig. 5), the poor-class precision is 142/142 = 100% and recall is 142/147 ≈ 96.6%; the good-class precision is 147/152 ≈ 96.7% and recall is 147/147 = 100%. Neither combination yields the Table 3 entries of 100% precision, 95.4% recall, and 95.4% sensitivity. The reported 97.7% accuracy is therefore not internally supported by the paper's own confusion matrix.
  3. [§3.1, Reference [23]] The dataset citation appears to be incorrect. Reference [23] refers to a GitHub repository titled "STORK: Spatio-Temporal non-uniformity prediction for knowledge graph embedding," which is not a blastocyst image dataset, and the STORK acronym definition given in §3.1 ("segmentation of temporal ovarian regions with K-means") seems invented. The manuscript must cite the actual source of the embryo images before any performance comparison or generalization claim can be taken seriously.
  4. [§4, first paragraph] The text states that during testing "20 images from each class were utilized" before augmentation, but Table 1 lists only 10 test images per class before augmentation. Since the entire evaluation is based on a very small number of original images, even the 90% pre-augmentation figure is fragile; the manuscript needs a clear statement of the actual test size and a proper explanation of why the reported numbers are not overfit.
minor comments (5)
  1. [Title] The title contains a spacing error: "T REATMENT" should read "TREATMENT".
  2. [§3.3, Table 2] The text says the LSTM output goes into "2 fully connected layers," but Table 2 lists only one fully connected layer; please align the text with the architecture table.
  3. [§3.2] The augmentation description says the process contributes to the creation of 1,372 images, but Table 1 reports 1,470 images after augmentation; this numerical inconsistency should be corrected.
  4. [§4, LIME validation] The fidelity score of 0.91 and mean IoU of 0.74 are reported without any description of the number of images annotated, the annotator protocol, or the baseline for comparison; please provide these details or temper the claims.
  5. [Abstract and Conclusion] The phrase "highlighting its potential for clinical application" is not supported by the evidence presented, since the test set is composed of augmented copies of a handful of real images; this wording should be softened or removed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is an empirical CNN-LSTM study; the reported accuracies are measurements, not predictions derived from fitted inputs, though the augmented test set limits generalization claims.

full rationale

No self-definitional or load-bearing self-citation was found. The CNN-LSTM model and LIME are standard methods; the LSTM description cites the authors' prior work [25] and [26] only for background. The central numerical claims (90% before augmentation, 97.7% after augmentation) are empirical results, not derived from an equation or from a fitted parameter renamed as a prediction. Hyperparameters were tuned on the training data, which is normal practice and does not make the test accuracy equivalent to a fit by construction. The main weakness, testing on 294 images generated by augmenting only 10 original test images per class, undermines generalization claims; the paper itself concedes that "synthetic augmentation cannot fully capture the complexity and diversity of real-world clinical data." That is a validity limitation, not a circularity. The internal inconsistency between the confusion matrix (five poor-as-good misclassifications) and Table 3 (precision=100%, recall=95.4%) is an accuracy or consistency issue, not a circular derivation. Therefore no circular step is identified.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central numerical claim is an empirical accuracy, not a derivation, so there are no free parameters in the physics sense. However, the augmentation hyperparameters and model hyperparameters are chosen by hand and tuned on the data. The paper relies on the domain assumptions that the dataset labels are correct and that augmented images are representative.

free parameters (2)
  • Augmentation rotation range and probability = -10 to 10 degrees, probability 0.5
    Chosen by hand to increase data diversity; affects all augmented images and thus the reported accuracy.
  • Model hyperparameters (learning rate, dropout, number of LSTM units) = 0.001, 0.4, 128
    The authors state these were fine-tuned through multiple iterations to achieve the best performance, so they are tuned on the data.
assumptions (3)
  • domain assumption The STORK dataset labels (good vs poor embryo) are accurate ground truth.
    The model's accuracy is measured against these labels; if labels are noisy, reported accuracy is misleading. The paper does not verify labels.
  • domain assumption Data augmentation produces realistic variations of embryos.
    The paper itself acknowledges synthetic augmentation cannot fully capture real-world data (Section 4), yet the central result relies on augmented data.
  • standard math LIME's local surrogate models faithfully approximate the CNN-LSTM's decisions.
    The fidelity score of 0.91 is used as evidence, but this is computed on the same data and does not validate the explanations externally.

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Cite this review

Pith. "Pith review of Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment." pith.science (2026). https://pith.science/paper/JHHAQAID

@misc{pith2026250606680,
  author       = {Pith},
  title        = {Pith review of: Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JHHAQAID}},
  note         = {Machine review of arXiv:2506.06680}
}
read the original abstract

Infertility has a considerable impact on individuals' quality of life, affecting them socially and psychologically, with projections indicating a rise in the upcoming years. In vitro fertilization (IVF) emerges as one of the primary techniques within economically developed nations, employed to address the rising problem of low fertility. Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer, yet this process is time-consuming and lacks efficiency. Blastocyst images provide a valuable resource for assessing embryo viability. In this study, we introduce an explainable artificial intelligence (XAI) framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture, referred to as CNN-LSTM. Utilizing deep learning, our model achieves high accuracy in embryo classification while maintaining interpretability through XAI.

Figures

Figures reproduced from arXiv: 2506.06680 by the authors.

Figure 1
Figure 1. Representation of good and poor embryos in STORK dataset [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Block diagram of the proposed CNN-LSTM model in selection of embryos [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Generation of visual explanations using LIME [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Performance analysis of CNN-LSTM model before augmentation in terms of (a) confusion chart (b) ROC [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Performance analysis of CNN-LSTM model after augmentation in terms of (a) confusion chart (b) ROC curve [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The top row represents the good embryo class, accompanied by top most features and visual explanations and [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: LIME visualizations—Correct classifications on left and right, misclassification in center [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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Reference graph

Works this paper leans on

30 extracted references · 29 canonical work pages

  1. [23]

    STORK: Spatio-Temporal non-uniformity prediction for knowledge graph embedding.https://github.com/ih-lab/ STORK, 2019

    Ihlab. STORK: Spatio-Temporal non-uniformity prediction for knowledge graph embedding.https://github.com/ih-lab/ STORK, 2019

    work page 2019

  2. [1]

    An automated blastocyst grading system using convolutional neural network and transfer learning

    Yusuf Abas Mohamed, Umi Kalsom Yusof, Iza Sazanita Isa, and Murizah Mohd Zain. An automated blastocyst grading system using convolutional neural network and transfer learning. In 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), pages 202–207. IEEE, 2023

    work page 2023

  3. [2]

    Time series prediction of viable embryo and automatic grading in ivf using deep learning

    Gargee Vaidya, Shreya Chandrasekhar, Ruchi Gajjar, Nagendra Gajjar, Deven Patel, and Manish Banker. Time series prediction of viable embryo and automatic grading in ivf using deep learning. The Open Biomedical Engineering Journal, 15(1), 2021

    work page 2021

  4. [3]

    The forgotten men: rising rates of male infertility urgently require new approaches for its prevention, diagnosis and treatment

    Vardit Ravitsky and Sarah Kimmins. The forgotten men: rising rates of male infertility urgently require new approaches for its prevention, diagnosis and treatment. Biology of reproduction, 101(5):872–874, 2019

    work page 2019

  5. [4]

    A swot analysis of human-and machine learning-based embryo assessment

    Huy Phuong Tran, Linh Nguyen-Hoang Tran, Huyen Thi Dang, Tuan Dinh Vu, Dat Tan Trinh, Bao The Pham, and Vu Ngoc Thanh Sang. A swot analysis of human-and machine learning-based embryo assessment. IEEE Access, 8:227466– 227481, 2020

    work page 2020

  6. [5]

    Is day 7 culture necessary for in vitro fertilization of cryopreserved/warmed human oocytes? Reproductive Biology and Endocrinology, 18:1–4, 2020

    Xiangli Niu, Cassie T Wang, Richard Li, Ghassan Haddad, and Weihua Wang. Is day 7 culture necessary for in vitro fertilization of cryopreserved/warmed human oocytes? Reproductive Biology and Endocrinology, 18:1–4, 2020

    work page 2020

  7. [6]

    Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs

    Luis Martínez-Granados, María Serrano, Antonio González-Utor, Nereyda Ortíz, Vicente Badajoz, Enrique Olaya, Nicolás Prados, Montse Boada, Jose A Castilla, and Special Interest Group in Quality of ASEBIR (Spanish Society for the Study of Reproductive Biology). Inter-laboratory agreement on embryo classification and clinical decision: conventional morpholo...

    work page 2017

  8. [7]

    Image processing approach for grading ivf blastocyst: A state-of-the-art review and future perspective of deep learning-based models

    Iza Sazanita Isa, Umi Kalsom Yusof, and Murizah Mohd Zain. Image processing approach for grading ivf blastocyst: A state-of-the-art review and future perspective of deep learning-based models. Applied Sciences, 13(2):1195, 2023

    work page 2023

Show all 30 references
  1. [8]

    Embryo selection in ivf

    Sebastiaan Mastenbroek, Fulco Van Der Veen, Abbas Aflatoonian, Bruce Shapiro, Patrick Bossuyt, and Sjoerd Repping. Embryo selection in ivf. Human Reproduction, 26(5):964–966, 2011

    2011

  2. [9]

    Optimising the outcome of embryo transfer

    Sunita Tandulwadkar, Madhuri Patil, and Sejal Naik. Optimising the outcome of embryo transfer. REPRODUCTIVE HEALTH, 2019

    2019

  3. [10]

    Cleavage-stage versus blastocyst-stage embryo transfer in assisted reproductive technology

    Demián Glujovsky, Andrea Marta Quinteiro Retamar, Cristian Roberto Alvarez Sedo, Agustín Ciapponi, Simone Cornelisse, and Deborah Blake. Cleavage-stage versus blastocyst-stage embryo transfer in assisted reproductive technology. Cochrane database of systematic reviews, (5), 2022

    2022

  4. [11]

    Computer-aided detection for pancreatic cancer diagnosis: radiological challenges and future directions

    Mark Ramaekers, Christiaan GA Viviers, Boris V Janssen, Terese AE Hellström, Lotte Ewals, Kasper van der Wulp, Joost Nederend, Igor Jacobs, Jon R Pluyter, Dimitrios Mavroeidis, et al. Computer-aided detection for pancreatic cancer diagnosis: radiological challenges and future ...

    2023

  5. [12]

    A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

    Xiaoxuan Liu, Livia Faes, Aditya U Kale, Siegfried K Wagner, Dun Jack Fu, Alice Bruynseels, Thushika Mahendiran, Gabriella Moraes, Mohith Shamdas, Christoph Kern, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medi...

    2019

  6. [13]

    Explainable computer vision analysis for embryo selection on blastocyst images

    Athanasios Kallipolitis, Melina Tziomaka, Dimitris Papadopoulos, and Ilias Maglogiannis. Explainable computer vision analysis for embryo selection on blastocyst images. In 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1–4. IEEE, 2022

    2022

  7. [14]

    A deep learning framework design for automatic blastocyst evaluation with multifocal images

    Shanshan Wang, Cong Zhou, Dan Zhang, Lei Chen, and Haixiang Sun. A deep learning framework design for automatic blastocyst evaluation with multifocal images. IEEE Access, 9:18927–18934, 2021

    2021

  8. [15]

    Shamrat, M

    F.J.M. Shamrat, M. Khalid, T.M. Qadah, M. Farrash, and H. Alshanbari. An explainable multi-objective hybrid machine learning model for reducing heart failure mortality. PeerJ Computer Science, 11:e2682, 2025

    2025

  9. [16]

    Advances in artificial intelligence and blockchain technologies for early detection of human diseases

    Shumaiya Akter Shammi, Pronab Ghosh, Ananda Sutradhar, F M Javed Mehedi Shamrat, Mohammad Ali Moni, and Thiago Eustaquio Alves de Oliveira. Advances in artificial intelligence and blockchain technologies for early detection of human diseases. IEEE Transactions on Computational...

    2025

  10. [17]

    Sutradhar, S

    A. Sutradhar, S. Akter, F.J.M. Shamrat, P. Ghosh, X. Zhou, M.Y .I.B. Idris, K. Ahmed, and M.A. Moni. Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable ai and hybrid machine learning techniques. Heliyon, 10(17), 2024

    2024

  11. [18]

    Majdi Khalid, F. M. Javed Mehedi Shamrat, Hanan Alshanbari, Majed Farrash, and Thamir M. Qadah. A dynamic selection hybrid model for advancing thyroid care with boo-st balancing method. IEEE Access, 12:78641–78656, 2024

    2024

  12. [19]

    Optimizing cnn hyperparameters for blastocyst quality assessment in small datasets

    Rifai Chai, Dadang Gunawan, et al. Optimizing cnn hyperparameters for blastocyst quality assessment in small datasets. IEEE Access, 10:88621–88631, 2022

    2022

  13. [20]

    Explainable ai based maternal health risk prediction using machine learning and deep learning

    Anika Rahman and Md Golam Rabiul Alam. Explainable ai based maternal health risk prediction using machine learning and deep learning. In 2023 IEEE World AI IoT Congress (AIIoT), pages 0013–0018. IEEE, 2023

    2023

  14. [21]

    An automated day 3 embryo grading based on morpholog- ical characteristics using cnn with transfer learning techniques

    Faisal Kevin Alkindy, Umi Kalsom Yusof, and Murizah Mohd Zain. An automated day 3 embryo grading based on morpholog- ical characteristics using cnn with transfer learning techniques. In 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSC...

    2023

  15. [22]

    Assessment of human blastocyst using deep learning algorithm

    Malathi Eswaran, P Balasubramanie, and V Pradeepa. Assessment of human blastocyst using deep learning algorithm. In 2023 2nd International Conference on Edge Computing and Applications (ICECAA), pages 691–697. IEEE, 2023

    2023

  16. [24]

    A comprehensive survey of image augmentation techniques for deep learning

    Mingle Xu, Sook Yoon, Alvaro Fuentes, and Dong Sun Park. A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognition, 137:109347, 2023

    2023

  17. [25]

    Raw waveform-based custom scalogram crnn in cardiac abnormality diagnosis

    Kodali Radha, Mohan Bansal, and Rajeev Sharma. Raw waveform-based custom scalogram crnn in cardiac abnormality diagnosis. IEEE Access, 2024

    2024

  18. [26]

    Explainable ai for cnn-lstm network in pcg-based valvular heart disease diagnosis

    Chelluri Divakar, Ramanu Harsha, Kodali Radha, Dulipalla Venkata Rao, Nadakuditi Madhavi, and Thiruveedhula Bharadwaj. Explainable ai for cnn-lstm network in pcg-based valvular heart disease diagnosis. In 2024 14th International Conference on Cloud Computing, Data Science & En...

    2024

  19. [27]

    Causability and explainability of artificial intelligence in medicine

    Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller. Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4):e1312, 2019

    2019

  20. [28]

    Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP , and more

    Aditya Bhattacharya. Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP , and more. Packt Publishing Ltd, 2022

    2022

  21. [29]

    Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality

    Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Charles L Bormann, Raghav Gupta, Rohan Pooniwala, Hemanth Kandula, Irene Souter, Irene Dimitriadis, and Hadi Shafiee. Evaluation of deep convolutional neural networks in classifying human embryo images based on their morpholo...

    2021

  22. [30]

    Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

    Elena Payá, Lorena Bori, Adrián Colomer, Marcos Meseguer, and Valery Naranjo. Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques. Computer methods and p...

    2022

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Reviewed August 7, 2026 · model on record in the stance chip above.