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REVIEW 3 major objections 1 minor 37 references

Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana

T0 review · 3 major / 1 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The submission's abstract claims that field studies in Nepal and Ghana show 85% of healthcare-AI respondents identifying ethical oversight as a key concern and 72% emphasizing localized governance, and proposes a Responsible AI framework ta

desk verdict The abstract and the full text are different papers; the submission is unverifiable as a package. read the letter →

arxiv 2508.12389 v1 pith:HVF7F4HK submitted 2025-08-17 cs.CY

Hari Krishna Neupane , Bhupesh Kumar Mishra This is my paper
classification cs.CY
keywords responsibleAIhealthcarelow-resourcesettingsethicaloversightlocalgovernanceNepalGhanapolicy
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 abstract describes a survey-based study of AI in healthcare in Nepal and Ghana, reporting that 85% of respondents saw ethical oversight as a key concern and 72% emphasized the need for localized governance. On that basis it proposes a draft Responsible AI Framework with ethical guidelines, regulatory compliance mechanisms, and contextual validation. The full text supplied under the submission identifier is an unrelated computational-pathology manuscript by different authors, so the survey, the data, and the framework do not appear in the submission. The intended pith is therefore the abstract's assertion that low-resource countries need locally grounded AI governance, grounded in field evidence. As submitted, that evidence is not available in the manuscript body.

What carries the argument

The abstract relies on three mechanisms: the field-study percentages (85% and 72%) as evidence of stakeholder priorities; the named draft Responsible AI Framework as the output; and the implied bridge between them, namely that these survey metrics justify the framework's components. In the supplied full text, none of these mechanisms appears; the only machinery present belongs to a graph-transformer architecture for whole-slide pathology images, which is irrelevant to the abstract's claim.

What would settle it

A reader can settle the question directly: open the supplied full text and search for 'survey', 'respondents', 'Nepal', or 'Ghana'. The body is IPGPhormer, an unrelated pathology manuscript, so none of the abstract's evidence is present. Any claim that the percentages are supported rests on a document that does not contain them.

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Extended reading notes

Core claim

The paper's central claim is that quantitative and qualitative field studies in Nepal and Ghana reveal critical obstacles to healthcare AI—data privacy, reliability, and trust—with 85% of respondents identifying ethical oversight as a key concern and 72% calling for localized governance structures. Building on these findings, the paper proposes a draft Responsible AI Framework for resource-constrained environments, whose key elements are ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches to mitigate bias and ensure equitable outcomes. However, the full text of the submission is an unrelated paper titled IPGPhormer on interpretable pathology graph-trans

Load-bearing premise

The load-bearing premise is that the submitted full text is the paper the abstract describes, and that the reported survey percentages come from an actual, representative field study in Nepal and Ghana.

Editorial extensions

If this is right

  • If the survey findings hold, healthcare AI deployment in Nepal and Ghana should be preceded by ethical oversight structures that local stakeholders themselves rank as the top concern.
  • The paper implies that imported governance models from high-resource settings will not suffice; localized governance structures are the reported demand.
  • It implies that data privacy, reliability, and trust are the binding constraints, so AI frameworks in low-resource settings should be evaluated first on these dimensions.
  • It implies that a contextually validated framework combining ethical guidelines and regulatory compliance can mitigate bias and improve equitable healthcare outcomes.

Reading between the lines

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

  • Editorial extension: the figures 85% and 72% carry no inferential weight without the survey instrument, sample size, respondent recruitment, and question wording; a reader should treat them as unverified numbers rather than generalizable statistics.
  • Editorial extension: because the full text is a different paper, the practical next step is to obtain the actual manuscript; if it exists, its data and framework draft would determine whether the abstract's claims survive.
  • Editorial extension: a testable design for the intended study would be a pre-registered cross-sectional survey of health workers and patients in both countries, with the survey instrument posted publicly so the reported percentages can be checked.
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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

3 major / 1 minor

Summary. The submission consists of an abstract claiming a survey-based evaluation in Nepal and Ghana, with quantitative findings (85% of respondents concerned about ethical oversight; 72% emphasizing localized governance) and a proposed Responsible AI (RAI) Framework for resource-constrained healthcare settings. The full text of the submission, however, is an entirely different paper: arXiv:2508.12381v2, 'IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysis', by Tang et al. That paper concerns computational pathology, survival analysis, and TCGA datasets; it contains no mention of Nepal, Ghana, surveys, respondents, ethical oversight, localized governance, or any RAI framework. Consequently, the abstract's central claims are completely unsupported by the submitted manuscript body.

Significance. If the survey and framework described in the abstract actually existed, the work could be relevant to Responsible AI policy in low-resource healthcare settings. However, in this submission there is no evidence base: no survey instrument, sample design, data, analysis, or framework description is present. The claimed percentages are therefore unverified and unreproducible. The unrelated computational pathology content does not contribute to the stated contribution. Because the central evidence and the proposed artifact are absent, the significance of the work cannot be assessed from this submission.

major comments (3)
  1. [Abstract vs. Full Text] The abstract claims 'Quantitative and qualitative field studies reveal critical metrics, including 85% of respondents identifying ethical oversight as a key concern, and 72% emphasizing the need for localized governance structures' and proposes 'a draft Responsible AI (RAI) Framework tailored to resource-constrained environments.' The full text (Sections 1–5) is a computational pathology paper on survival analysis using whole-slide images, with no reference to Nepal, Ghana, healthcare stakeholders, surveys, ethical oversight, localized governance, or RAI. The central empirical claims have no supporting content in the submission.
  2. [Methodology and Data] No research methodology is provided for the claimed field studies. There is no description of the survey instrument, sampling strategy, respondent population, sample size, data collection procedures, ethical approvals, or analysis methods. Even if one treated the abstract as a standalone claim, the percentages cannot be checked for internal consistency, generalizability, or reproducibility. This is a load-bearing omission because the entire contribution rests on these survey findings.
  3. [Proposed RAI Framework] The abstract states that a draft RAI Framework is proposed, with key elements including 'ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches.' None of these elements are elaborated, justified, or connected to the survey results anywhere in the manuscript. The framework is not derived from, or even referenced in, the full text. A framework that is merely listed in the abstract, without content, cannot be evaluated or used.
minor comments (1)
  1. [Bibliographic Consistency] The full text carries the running header 'TANG ET AL.: IPGPHORMER: INTERPRETABLE SURVIVAL ANALYSIS' and a reference list corresponding to the pathology paper. The submission metadata and abstract are inconsistent with this content; this should be resolved before any resubmission.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation present; abstract and full text are mismatched, but mismatch is an evidence/support problem, not circularity.

full rationale

The submission's abstract claims survey-based results from Nepal and Ghana and a draft Responsible AI Framework, but the full text provided is an unrelated computational pathology paper (IPGPhormer) with no mention of surveys, Nepal, Ghana, or AI governance. This is a serious integrity/support problem, but it is not a circularity problem: there is no derivation chain in which a claimed prediction or framework component is shown, by the paper's own equations or definitions, to be equivalent to its inputs. No fitted parameter is renamed as a prediction, no self-citation is load-bearing, and no uniqueness theorem is imported from the authors' prior work. The abstract's statistics (85%, 72%) are asserted without accompanying methodology or data, which makes them unverifiable, but unverified evidence is not the same as circular reasoning. Under the hard rules, circularity requires quoting the paper and exhibiting a specific reduction (Eq. X = Eq. Y by construction, or fitted parameter relabeled as prediction). No such reduction exists in the provided text. The honest finding is therefore 'no significant circularity,' score 0, with the caveat that the manuscript body does not support the abstract at all.

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

Only the abstract is available for the claimed survey; its implicit assumptions are listed here. The full text provides no survey content, so no additional axioms can be extracted from it for the abstract's claim.

assumptions (1)
  • domain assumption Survey responses from a sample in Nepal and Ghana are representative of healthcare stakeholders in low-resource countries.
    The abstract reports percentages as if they reflect population attitudes, but no sampling methodology appears in the full text (which is a different paper). This is an implicit representativeness assumption.

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Cite this review

Pith. "Pith review of Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana." pith.science (2026). https://pith.science/paper/HVF7F4HK

@misc{pith2026250812389,
  author       = {Pith},
  title        = {Pith review of: Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HVF7F4HK}},
  note         = {Machine review of arXiv:2508.12389}
}
read the original abstract

The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, including 85% of respondents identifying ethical oversight as a key concern, and 72% emphasizing the need for localized governance structures. Building on these findings, we propose a draft Responsible AI (RAI) Framework tailored to resourceconstrained environments in these countries. Key elements of the framework include ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches to mitigate bias and ensure equitable healthcare outcomes.

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Reference graph

Works this paper leans on

37 extracted references · 30 canonical work pages

  1. [1]

    Springer- Verlag, 2008

    Odd O Aalen.Survival and Event History Analysis: A Process Point of View. Springer- Verlag, 2008

    work page 2008

  2. [2]

    TANG ET AL.: IPGPHORMER: INTERPRETABLE SURVIV AL ANAL YSIS11 Histopathology whole slide image analysis with heterogeneous graph representation learning

    Tsai Hor Chan, Fernando Julio Cendra, Lan Ma, Guosheng Yin, and Lequan Yu. TANG ET AL.: IPGPHORMER: INTERPRETABLE SURVIV AL ANAL YSIS11 Histopathology whole slide image analysis with heterogeneous graph representation learning. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15661–15670, 2023

    work page 2023

  3. [3]

    Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks

    Richard J Chen, Ming Y Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y Chen, Drew FK Williamson, and Faisal Mahmood. Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, F...

    work page 2021

  4. [4]

    Scaling vision transformers to gigapixel images via hierarchical self-supervised learning

    Richard J Chen, Chengkuan Chen, Yicong Li, Tiffany Y Chen, Andrew D Trister, Rahul G Krishnan, and Faisal Mahmood. Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 16144–16155, 2022

    work page 2022

  5. [5]

    A new initiative on precision medicine.New England journal of medicine, 372(9):793–795, 2015

    Francis S Collins and Harold Varmus. A new initiative on precision medicine.New England journal of medicine, 372(9):793–795, 2015

    work page 2015

  6. [6]

    Regression models and life-tables.Journal of the Royal Statistical Society: Series B (Methodological), 34(2):187–202, 1972

    David R Cox. Regression models and life-tables.Journal of the Royal Statistical Society: Series B (Methodological), 34(2):187–202, 1972

    1972

  7. [7]

    Cross-scale multi-instance learning for pathological image diagnosis.Medical image analysis, 94: 103124, 2024

    Ruining Deng, Can Cui, Lucas W Remedios, Shunxing Bao, R Michael Womick, Sophie Chiron, Jia Li, Joseph T Roland, Ken S Lau, Qi Liu, et al. Cross-scale multi-instance learning for pathological image diagnosis.Medical image analysis, 94: 103124, 2024

    work page 2024

  8. [8]

    Efficient and accu- rate frailty model approach for genome-wide survival association analysis in large-scale biobanks.Nature communications, 13(1):5437, 2022

    Rounak Dey, Wei Zhou, Tuomo Kiiskinen, Aki Havulinna, Amanda Elliott, Juha Kar- jalainen, Mitja Kurki, Ashley Qin, FinnGen, Seunggeun Lee, et al. Efficient and accu- rate frailty model approach for genome-wide survival association analysis in large-scale biobanks.Nature communications, 13(1):5437, 2022

    work page 2022

Show all 37 references
  1. [9]

    Survival analysis—time-to-event data and censoring.Nat Methods, 19 (8):903, 2022

    Tanujit Dey, Stuart R Lipsitz, Zara Cooper, Quoc-Dien Trinh, Martin Krzywinski, and Naomi Altman. Survival analysis—time-to-event data and censoring.Nat Methods, 19 (8):903, 2022

    2022

  2. [10]

    Pannuke: an open pan-cancer histology dataset for nuclei instance segmen- tation and classification

    Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benet, Ali Khuram, and Nasir Rajpoot. Pannuke: an open pan-cancer histology dataset for nuclei instance segmen- tation and classification. InDigital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, P...

    2019

  3. [11]

    Prediction of prognosis for prostatic ade- nocarcinoma by combined histological grading and clinical staging.The Journal of urology, 111(1):58–64, 1974

    Donald F Gleason and George T Mellinger. Prediction of prognosis for prostatic ade- nocarcinoma by combined histological grading and clinical staging.The Journal of urology, 111(1):58–64, 1974

    1974

  4. [12]

    Hover-net: Simultaneous segmentation and classifi- cation of nuclei in multi-tissue histology images.Medical image analysis, 58:101563, 2019

    Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot. Hover-net: Simultaneous segmentation and classifi- cation of nuclei in multi-tissue histology images.Medical image analysis, 58:101563, 2019. 12TANG ET AL.: IPGPHORMER: ...

    2019

  5. [13]

    Slab: Efficient transform- ers with simplified linear attention and progressive re-parameterized batch normaliza- tion.arXiv preprint arXiv:2405.11582, 2024

    Jialong Guo, Xinghao Chen, Yehui Tang, and Yunhe Wang. Slab: Efficient transform- ers with simplified linear attention and progressive re-parameterized batch normaliza- tion.arXiv preprint arXiv:2405.11582, 2024

    2024 arXiv

  6. [14]

    Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Statistics in medicine, 15(4):361–387, 1996

    Frank E Harrell Jr, Kerry L Lee, and Daniel B Mark. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Statistics in medicine, 15(4):361–387, 1996

    1996

  7. [15]

    Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis

    Wentai Hou, Lequan Yu, Chengxuan Lin, Helong Huang, Rongshan Yu, Jing Qin, and Liansheng Wang. Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. InProceedings of the AAAI conference on artificial intel...

    2022

  8. [16]

    Attention-based deep multiple instance learning

    Maximilian Ilse, Jakub Tomczak, and Max Welling. Attention-based deep multiple instance learning. InInternational conference on machine learning, pages 2127–2136. PMLR, 2018

    2018

  9. [17]

    The single-cell pathology landscape of breast cancer.Nature, 578(7796):615– 620, 2020

    Hartland W Jackson, Jana R Fischer, Vito RT Zanotelli, H Raza Ali, Robert Mechera, Savas D Soysal, Holger Moch, Simone Muenst, Zsuzsanna Varga, Walter P Weber, et al. The single-cell pathology landscape of breast cancer.Nature, 578(7796):615– 620, 2020

    2020

  10. [18]

    Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology

    Saarthak Kapse, Pushpak Pati, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel Saltz, Dimitris Samaras, Rajarsi R Gupta, and Prateek Prasanna. Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology. InPro- ceedings of the IEEE/CVF Conferenc...

    2024

  11. [19]

    Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  12. [20]

    Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.Nature Biomedical Engineering, pages 1–15, 2022

    Yongju Lee, Jeong Hwan Park, Sohee Oh, Kyoungseob Shin, Jiyu Sun, Minsun Jung, Cheol Lee, Hyojin Kim, Jin-Haeng Chung, Kyung Chul Moon, et al. Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.Nature Biome...

    2022

  13. [21]

    Dual-stream multiple instance learning net- work for whole slide image classification with self-supervised contrastive learning

    Bin Li, Yin Li, and Kevin W Eliceiri. Dual-stream multiple instance learning net- work for whole slide image classification with self-supervised contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14318–14328, 2021

    2021

  14. [22]

    Dynamic graph representation with knowledge-aware attention for histopathology whole slide image analysis

    Jiawen Li, Yuxuan Chen, Hongbo Chu, Qiehe Sun, Tian Guan, Anjia Han, and Yonghong He. Dynamic graph representation with knowledge-aware attention for histopathology whole slide image analysis. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognitio...

    2024

  15. [23]

    Generative causal explanations for graph neu- ral networks

    Wanyu Lin, Hao Lan, and Baochun Li. Generative causal explanations for graph neu- ral networks. InInternational Conference on Machine Learning, pages 6666–6679. PMLR, 2021

    2021

  16. [24]

    Deep learning for survival analysis in breast cancer with whole slide image data.Bioinformatics, 38(14):3629–3637, 2022

    Huidong Liu and Tahsin Kurc. Deep learning for survival analysis in breast cancer with whole slide image data.Bioinformatics, 38(14):3629–3637, 2022. TANG ET AL.: IPGPHORMER: INTERPRETABLE SURVIV AL ANAL YSIS13

    2022

  17. [25]

    Advmil: Adversarial multiple instance learning for the survival analysis on whole-slide images.Medical Image Analysis, 91:103020, 2024

    Pei Liu, Luping Ji, Feng Ye, and Bo Fu. Advmil: Adversarial multiple instance learning for the survival analysis on whole-slide images.Medical Image Analysis, 91:103020, 2024

    2024

  18. [26]

    Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570, 2021

    Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570, 2021

    2021

  19. [27]

    Evaluation of survival data and two new rank order statistics arising in its consideration.Cancer Chemother Rep, 50(3):163–170, 1966

    Nathan Mantel et al. Evaluation of survival data and two new rank order statistics arising in its consideration.Cancer Chemother Rep, 50(3):163–170, 1966

    1966

  20. [28]

    Grasp: graph-structured pyramidal whole slide image repre- sentation.arXiv preprint arXiv:2402.03592, 2024

    Ali Khajegili Mirabadi, Graham Archibald, Amirali Darbandsari, Alberto Contreras- Sanz, Ramin Ebrahim Nakhli, Maryam Asadi, Allen Zhang, C Blake Gilks, Peter Black, Gang Wang, et al. Grasp: graph-structured pyramidal whole slide image repre- sentation.arXiv preprint arXiv:2402...

    2024 arXiv

  21. [29]

    Hierarchical graph representations in digital pathology

    Pushpak Pati, Guillaume Jaume, Antonio Foncubierta-Rodriguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Maryse Fiche, Estelle Dubruc, Daniel Riccio, et al. Hierarchical graph representations in digital pathology. Medical image analysis, 75:10...

    2022

  22. [30]

    Transmil: Transformer based correlated multiple instance learning for whole slide im- age classification.Advances in neural information processing systems, 34:2136–2147, 2021

    Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al. Transmil: Transformer based correlated multiple instance learning for whole slide im- age classification.Advances in neural information processing systems, 34:2136–2147, 2021

    2021

  23. [31]

    Attention is all you need.Advances in neural information processing systems, 30, 2017

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

    2017

  24. [32]

    Graph attention networks.arXiv preprint arXiv:1710.10903, 2017

    Petar Veli ˇckovi´c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks.arXiv preprint arXiv:1710.10903, 2017

    2017 arXiv

  25. [33]

    Transformer-based unsupervised contrastive learning for histopathological image classification.Medical image analysis, 81:102559, 2022

    Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Jun- zhou Huang, and Xiao Han. Transformer-based unsupervised contrastive learning for histopathological image classification.Medical image analysis, 81:102559, 2022

    2022

  26. [34]

    Zhikang Wang, Qian Gao, Xiaoping Yi, Xinyu Zhang, Yiwen Zhang, Daokun Zhang, Pietro Liò, Chris Bain, Richard Bassed, Shanshan Li, et al. Surformer: An inter- pretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images....

    2023

  27. [35]

    Survtrace: Transformers for survival analysis with com- peting events

    Zifeng Wang and Jimeng Sun. Survtrace: Transformers for survival analysis with com- peting events. InProceedings of the 13th ACM international conference on bioinfor- matics, computational biology and health informatics, pages 1–9, 2022

    2022

  28. [36]

    The cancer genome atlas pan-cancer analysis project.Nature genetics, 45(10):1113–1120, 2013

    John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Shaw, Brad A Ozen- berger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, and Joshua M Stuart. The cancer genome atlas pan-cancer analysis project.Nature genetics, 45(10):1113–1120, 2013. 14TANG ET AL.: IPGPHORMER: INTE...

    2013

  29. [37]

    A graph-transformer for whole slide image classi- fication.IEEE transactions on medical imaging, 41(11):3003–3015, 2022

    Yi Zheng, Rushin H Gindra, Emily J Green, Eric J Burks, Margrit Betke, Jennifer E Beane, and Vijaya B Kolachalama. A graph-transformer for whole slide image classi- fication.IEEE transactions on medical imaging, 41(11):3003–3015, 2022

    2022

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