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REVIEW 5 major objections 4 minor 47 references

DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

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

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Pith's one-line read DLiPath is introduced as the first public benchmark that grades donor-liver whole-slide images on six pathological features and shows weak-supervision models can score most of them accurately.

desk verdict The donor-liver benchmark is a good idea, but the paper's own Table 9 contradicts the 636-WSI claim, so none of the baseline numbers can be trusted until the label inventory is reconciled. read the letter →

arxiv 2506.03185 v1 pith:6F6HZMVR submitted 2025-05-30 eess.IV cs.AIcs.CVq-bio.QM

Liangrui Pan , Xingchen Li , Zhongyi Chen , Ling Chu , Shaoliang Peng This is my paper
classification eess.IVcs.AIcs.CVq-bio.QM
keywords donorliverhistopathologyimageswholeslidebenchmarkmultipleinstancelearningsteatosistransplantationpathologicalgrading
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

DLiPath is a new public benchmark for donor-liver assessment built from 636 whole-slide images of 304 donor livers, with expert four-grade labels for cholestasis, portal tract fibrosis, portal inflammation, total steatosis, macrovesicular steatosis, and hepatocellular ballooning. The paper's central claim is that this is the first benchmark tailored to donor-liver histopathology, where existing public liver datasets concentrate on cancer, and that weak-supervision models can grade these six features from slide-level labels alone. On this dataset, several attention-based multiple-instance learning models reach high accuracy, above 0.93 on cholestasis and fibrosis and above 0.98 on total steatosis, while hepatocellular ballooning proves more difficult. If the benchmark holds up, it gives transplant pathology a shared dataset for building rapid, objective intraoperative graft assessment and a concrete target for the hardest indicators.

What carries the argument

The load-bearing mechanism is a weak-supervision pipeline from tissue to label. A whole-slide image is tiled into 256x256 patches, each patch is embedded into a 768-dimensional vector by a pretrained transformer-based histopathology feature extractor, and the slide becomes a bag of these patch embeddings. Attention-based multiple-instance learning aggregators then weight the instances and produce the four-grade classification for each of the six indicators. The key point is that only slide-level expert labels are required, not patch-level annotations, which is what makes large-scale donor-liver benchmarking practical.

What would settle it

Re-grade a stratified random sample of slides with two independent pathologists using the paper's grading criteria: if weighted agreement is low (for example, kappa below 0.4), or if release inspection shows many slides missing labels for one or more features, then the reported baseline accuracies rest on labels that cannot carry them. A direct starting check is to reconcile the appendix's per-feature counts of about 260 with the claimed 636 labeled slides.

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Extended reading notes

Core claim

DLiPath is presented as the first public benchmark for comprehensive donor liver assessment from histopathology whole-slide images. It consists of 636 WSIs from 304 donor-liver patients, graded by multiple pathologists on six indicators using a four-point scale (none, mild, moderate, severe), and the release includes both the whole-slide images and precomputed patch features. Each indicator is treated as a multi-class classification problem under a multiple-instance learning formulation, and nine weakly supervised MIL models are compared with five-fold cross-validation. The paper's supported conclusion is that weakly supervised models already score several donor-liver indicators at high accuracy, with cholestasis, portal tract fibrosis, and total steatosis the strongest and hepatocellular ballooning and macrovesicular steatosis the remaining open problems, making automated intraoperative graft assessment a feasible next step.

Load-bearing premise

The benchmark's value depends on the expert labels being complete and trustworthy for every slide and every feature, even though the paper acknowledges that histopathologic grading is subjective and inconsistent on low-quality slides.

Editorial extensions

If this is right

  • DLiPath gives computational pathology a shared testbed where donor-liver methods can be compared directly on six clinically defined tasks instead of on private slide collections.
  • Because the pipeline needs only slide-level labels, hospitals without pixel-level annotations can reproduce the benchmark using the released patch features, lowering the entry barrier for automated graft assessment.
  • The strongest baselines exceed 0.98 accuracy on total steatosis and 0.93 on cholestasis and fibrosis, making those tasks credible candidates for intraoperative decision support.
  • Hepatocellular ballooning remains the hardest task, with the best baseline near 0.69 accuracy, so the benchmark identifies where label quality or feature representation must improve.
  • The authors project that automated donor-liver assessment of this kind could reduce graft discard rates by 20 to 30 percent by making borderline graft decisions faster and more consistent.

Reading between the lines

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

  • The appendix's per-feature label counts sum to roughly 260 per feature rather than 636; if those counts reflect slides without labels for some features, the effective per-task dataset is much smaller than the headline number and all baseline scores should be read against that smaller denominator.
  • Because the paper does not report inter-observer agreement, an immediate companion study would be to release the original individual pathologist grades and agreement statistics, letting the community separate model error from label noise.
  • A natural next test the authors did not run is ordinal regression or continuous percentage-based regression on the thresholds in the grading table, which may outperform the four-class softmax formulation used here.
  • The benchmark's single feature extractor and consensus labels mean transferability to other scanners, stains, and grading conventions remains untested; multi-center slides would reveal how much of the reported accuracy is institution-specific.
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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

5 major / 4 minor

Summary. This manuscript introduces DLiPath, described as the first public benchmark for donor liver assessment from histopathology whole-slide images. The authors report collecting 636 WSIs from 304 donor liver patients, with expert annotations for cholestasis, portal tract fibrosis, portal inflammation, total steatosis, macrovesicular steatosis, and hepatocellular ballooning. Nine multiple-instance learning baselines are evaluated on six four-grade classification tasks using five-fold cross-validation, with TransMIL reported as the strongest model. The paper also states that data and code are available at a GitHub repository.

Significance. If the dataset and annotations were as described, DLiPath would fill a genuine gap: no existing public benchmark targets donor liver assessment, and the six features are clinically relevant. The use of nine established MIL baselines and the intent to publicly release data and code are strengths. However, the manuscript's own label distribution (Table 9) contradicts the stated dataset size, and the reported experimental results contain internal metric inconsistencies and are presented without uncertainty estimates. As a result, the central contribution—a reliable 636-WSI expert-annotated benchmark with trustworthy baseline results—is not supported by the evidence in the manuscript.

major comments (5)
  1. [Section 3.1 and Table 9] The label counts in Table 9 sum to approximately 261–263 per feature (cholestasis 131+128+2+0=261; portal tract fibrosis 187+76+0+0=263; portal inflammation 233+29+1+0=263; total steatosis 160+93+10+0=263; macrovesicular steatosis 184+66+12+1=263; hepatocellular ballooning 3+45+133+81=262), not the 636 WSIs or 304 patients claimed in the Abstract and Section 3.1. This directly contradicts the statement that every WSI in DLiPath is expert-annotated, and it implies that Tables 2–7 may be computed on an undocumented subset of roughly 41% of the corpus. The manuscript must state the exact number of annotated WSIs per feature, explain the discrepancy, and re-run all baselines on the actual annotated corpus.
  2. [Appendix A.3 and Section 3.1] The evaluation protocol does not specify whether five-fold cross-validation splits are at WSI level or patient level. Since 636 WSIs come from 304 patients (about 2.1 WSIs per patient on average), WSI-level random splitting can place sections from the same donor in both training and test folds, inflating the reported accuracies. Please describe the split and use patient-level stratification.
  3. [Tables 2–7 and Section 4.3] Metrics are reported as means over five folds without standard deviations or any uncertainty estimate, despite extreme class imbalance: cholestasis has only 2 moderate and 0 severe cases; portal tract fibrosis has 0 moderate and 0 severe cases; portal inflammation has 1 moderate and 0 severe cases; total steatosis has 0 severe cases; macrovesicular steatosis has 1 severe case. Several reported AUCs are below the random baseline (e.g., ABMIL cholestasis AUC=0.4510, ABMIL portal inflammation AUC=0.3675, ABMIL macrovesicular AUC=0.4748, ILRA hepatocellular AUC=0.4761), which is not discussed. The claim that models achieve high accuracy is therefore not supported without additional per-fold results and a clear handling of empty or missing classes.
  4. [Tables 5 and 7] Some reported metrics are internally inconsistent. In Table 5, ABMIL has Precision=0.5694 and Recall=0.9837, which gives F1≈0.721, not the reported 0.6781. In Table 7, the ILRA row reports Recall=0.6845 and F1=0.8715, while the text states Recall=0.8715 and F1=0.5859; neither F1 is consistent with the stated precision and recall. In addition, the Section 5.2 sentence on DGRMIL and DSMIL accuracies (0.8955 and 0.9113) does not match Table 3, where DSMIL's accuracy is 0.8577. Such inconsistencies undermine confidence in the quantitative baseline comparisons.
  5. [Section 6] The authors acknowledge that histopathological grading is subjective and inconsistent, particularly on low-quality WSIs, yet no inter-observer agreement statistic (e.g., kappa) or adjudication reliability measure is reported. Given the central role of expert labels in a benchmark, the absence of label-quality metrics is a substantive gap; please provide agreement or consensus statistics and a per-feature label reliability analysis.
minor comments (4)
  1. [Section 3.2] There is a typo in 'histopathological asseassessment'; please correct it.
  2. [Table 1] The heading 'Post-mortem Donor Liver Biopsy' is confusing because donor liver sections are not necessarily post-mortem; please align the terminology with the rest of the text.
  3. [Section 4.1] The softmax equation is garbled in the provided text; please ensure proper typesetting of the normalization term.
  4. [Appendix A.2] Table 9 should be referenced in the main text so that readers can locate the label distribution and understand how it relates to the claimed 636 WSIs.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the benchmark evaluation is standard empirical practice and no claim reduces to its own inputs by construction.

full rationale

This is a dataset-and-benchmark paper, not a derivation paper. The nine MIL baselines are trained and evaluated on the same expert-annotated dataset via five-fold cross-validation, which is standard empirical benchmarking and is not circular: the reported accuracies are measured on held-out folds, not on fitted quantities renamed as predictions. No parameter is fitted to a subset of data and then reported as a prediction of a closely related quantity. The paper does not invoke a uniqueness theorem, does not justify a central premise solely by a self-citation, and does not smuggle in an ansatz via a citation. The authors cite their own prior work [28] only as a related application, not as load-bearing support for the benchmark's validity. The manuscript's internal inconsistency between the claimed 636 WSIs / 304 patients and Table 9's label counts (which sum to roughly 261-263 per feature) is a serious data-integrity and documentation concern, but it is not circularity: the benchmark results do not reduce by construction to the dataset definition; they depend on an unverified label inventory. A data-consistency failure or missing external validation is a correctness risk, not a self-referential derivation. Therefore no circular step can be exhibited under the required standard, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The benchmark rests on the assumptions that pathologist-assigned four-grade labels are correct and complete for all WSIs, that the ordinal scale is meaningful for each feature despite almost empty moderate/severe classes, that CTransPath embeddings capture all diagnostically relevant signal, and that predicting expert grades is a suitable proxy for clinical transplant utility. No free parameters are fitted to support a derivation; the only numbers in the paper are evaluation scores. No new entities are postulated.

assumptions (4)
  • domain assumption Expert pathologist scores are treated as ground truth for all six features.
    Section 3.2 assigns labels by agreement of two junior pathologists plus senior adjudication, but the paper itself notes inter- and intra-observer variability and low-quality WSI issues in Section 6.
  • domain assumption The four-grade ordinal scale (none/mild/moderate/severe) is applicable and informative for every indicator.
    Table 1 defines the scale, yet label distributions in Table 9 show almost no samples in moderate/severe classes for several indicators.
  • domain assumption CTransPath patch features retain the diagnostically relevant information for all six indicators.
    Section 3.3 uses CTransPath embeddings as the only input; no analysis shows these features suffice for cholestasis or ballooning.
  • domain assumption Correlation of the six features with transplant outcomes implies that predicting expert grades is a clinically meaningful benchmark target.
    Section 1 cites outcome correlations, but the benchmark measures prediction of expert grades, not transplant outcomes directly.

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Cite this review

Pith. "Pith review of DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset." pith.science (2026). https://pith.science/paper/6F6HZMVR

@misc{pith2026250603185,
  author       = {Pith},
  title        = {Pith review of: DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6F6HZMVR}},
  note         = {Machine review of arXiv:2506.03185}
}
read the original abstract

Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, rapidly and accurately obtaining these assessments intraoperatively poses a significant challenge for pathologists. Features in donor liver biopsies, such as portal tract fibrosis, total steatosis, macrovesicular steatosis, and hepatocellular ballooning are correlated with transplant outcomes, yet quantifying these indicators suffers from substantial inter- and intra-observer variability. To address this, we introduce DLiPath, the first benchmark for comprehensive donor liver assessment based on a histopathology image dataset. We collected and publicly released 636 whole slide images from 304 donor liver patients at the Department of Pathology, the Third Xiangya Hospital, with expert annotations for key pathological features (including cholestasis, portal tract fibrosis, portal inflammation, total steatosis, macrovesicular steatosis, and hepatocellular ballooning). We selected nine state-of-the-art multiple-instance learning (MIL) models based on the DLiPath dataset as baselines for extensive comparative analysis. The experimental results demonstrate that several MIL models achieve high accuracy across donor liver assessment indicators on DLiPath, charting a clear course for future automated and intelligent donor liver assessment research. Data and code are available at https://github.com/panliangrui/ACM_MM_2025.

Figures

Figures reproduced from arXiv: 2506.03185 by the authors.

Figure 1
Figure 1. Six pathological indicators for donor liver assessment. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

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Works this paper leans on

47 extracted references · 45 canonical work pages

  1. [1]

    Vatche G Agopian, Michael P Harlander-Locke, Daniela Markovic, Wethit Dum- ronggittigule, Victor Xia, Fady M Kaldas, Ali Zarrinpar, Hasan Yersiz, Douglas G Farmer, Jonathan R Hiatt, et al. 2018. Evaluation of early allograft function using the liver graft assessment following transplantation risk score model.JAMA surgery153, 5 (2018), 436–444

    work page 2018

  2. [2]

    Ola Ahmed and MB Doyle. 2021. Liver transplantation: expanding the donor and recipient pool. (2021)

    work page 2021

  3. [3]

    F Arslan, B Keogh, P McGuirk, and AE Parker. 2010. TLR2 and TLR4 in ischemia reperfusion injury.Mediators of inflammation2010, 1 (2010), 704202

    work page 2010

  4. [4]

    Mamatha Bhat, Madhumitha Rabindranath, Beatriz Sordi Chara, and Douglas A Simonetto. 2023. Artificial intelligence, machine learning, and deep learning in liver transplantation.Journal of hepatology78, 6 (2023), 1216–1233

    work page 2023

  5. [5]

    Veronika Cheplygina, Lauge Sørensen, David MJ Tax, Marleen de Bruijne, and Marco Loog. 2015. Label stability in multiple instance learning. InMedical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18. Springer, 539–546

    work page 2015

  6. [6]

    Michael JJ Chu, Anthony JR Hickey, Anthony RJ Phillips, and Adam SJR Bartlett

    work page

  7. [7]

    Michael JJ Chu, Rakesh Premkumar, Anthony JR Hickey, Yannan Jiang, Brett Delahunt, Anthony RJ Phillips, and Adam SJR Bartlett. 2016. Steatotic livers are susceptible to normothermic ischemia-reperfusion injury from mitochondrial Complex-I dysfunction.World journal of gastroenterology22, 19 (2016), 4673

    work page 2016

  8. [8]

    Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L Moreira, Narges Razavian, and Aristotelis Tsirigos. 2018. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.Nature medicine24, 10 (2018), 1559–1567

    work page 2018

Show all 47 references
  1. [9]

    Miao Cui and David Y Zhang. 2021. Artificial intelligence and computational pathology.Laboratory Investigation101, 4 (2021), 412–422

    2021

  2. [10]

    Jeffrey S Damrauer, Markia A Smith, Vonn Walter, Aatish Thennavan, Lisle E Mose, Sara R Selitsky, and Katherine A Hoadley. 2021. Genomic characterization of rare molecular subclasses of hepatocellular carcinoma.Communications Biology 4, 1 (2021), 1150

    2021

  3. [11]

    Zhaolong Du, Shasha Mao, Xuequan Lu, Mengnan Qi, Yimeng Zhang, Jing Gu, and Licheng Jiao. [n. d.]. Rethinking Multiple-Instance Learning From Feature Space to Probability Space. InThe Thirteenth International Conference on Learning Representations

  4. [12]

    Antonia D’Errico, Mattia Riefolo, Matteo Serenari, Vanessa De Pace, Giacomo Santandrea, Melissa Monica, Carlo de Cillia, Matteo Ravaioli, Matteo Cescon, Francesco Vasuri, et al. 2020. The histological assessment of liver fibrosis in grafts from extended criteria donors predict...

    2020

  5. [13]

    Alessandro Gambella, Massimo Salvi, Luca Molinaro, Damiano Patrono, Paola Cassoni, Mauro Papotti, Renato Romagnoli, and Filippo Molinari. 2024. Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms.Journal of Hepatology...

    2024

  6. [14]

    Seren M Gedallovich, Daniela P Ladner, and Lisa B VanWagner. 2022. Liver transplantation in the era of non-alcoholic fatty liver disease/metabolic (dysfunc- tion) associated fatty liver disease: The dilemma of the steatotic liver graft on transplantation and recipient survival...

    2022

  7. [15]

    Tiancheng He, Joy Nolte Fong, Linda W Moore, Chika F Ezeana, David Victor, Mukul Divatia, Matthew Vasquez, R Mark Ghobrial, and Stephen TC Wong. 2021. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular canc...

    2021

  8. [16]

    Le Hou, Dimitris Samaras, Tahsin M Kurc, Yi Gao, James E Davis, and Joel H Saltz. 2016. Patch-based convolutional neural network for whole slide tissue image classification. InProceedings of the IEEE conference on computer vision and pattern recognition. 2424–2433

    2016

  9. [17]

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

    2018

  10. [18]

    Yunsang Joo, Hyun-Cheol Park, O-Joun Lee, Changhan Yoon, Moon Hyung Choi, and Chang Choi. 2023. Classification of liver fibrosis from heterogeneous ultrasound image.IEEE Access11 (2023), 9920–9930

    2023

  11. [19]

    Yoo Jung Kim, Hyungjoon Jang, Kyoungbun Lee, Seongkeun Park, Sung-Gyu Min, Choyeon Hong, Jeong Hwan Park, Kanggeun Lee, Jisoo Kim, Wonjae Hong, et al. 2021. PAIP 2019: Liver cancer segmentation challenge.Medical image analysis67 (2021), 101854

    2021

  12. [20]

    K. Lekshmi. 2020. Hepatic Histopathology Dataset. Mendeley Data, V1. https: //doi.org/10.17632/7mmgm5fht8.1

    2020 doi

  13. [21]

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

    2021

  14. [22]

    Tiancheng Lin, Hongteng Xu, Canqian Yang, and Yi Xu. 2022. Interventional multi-instance learning with deconfounded instance-level prediction. InProceed- ings of the AAAI conference on artificial intelligence, Vol. 36. 1601–1609

    2022

  15. [23]

    Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, and Chang-Wen Chen. 2023. Interventional bag multi-instance learning on whole-slide pathological images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 19830–19839

    2023

  16. [24]

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer us- ing shifted windows. InProceedings of the IEEE/CVF international conference on computer vision. 10012–10022

    2021

  17. [25]

    Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. 2021. Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering5, 6 (2021), 555–570

    2021

  18. [26]

    Anant Madabhushi and George Lee. 2016. Image analysis and machine learning in digital pathology: Challenges and opportunities.Medical image analysis33 (2016), 170–175

    2016

  19. [27]

    Antonio Nocito, Ashraf Mohammad El-Badry, and Pierre-Alain Clavien. 2006. When is steatosis too much for transplantation?Journal of hepatology45, 4 (2006), 494–499

    2006

  20. [28]

    Liangrui Pan, Qingchun Liang, Wenwu Zeng, Yijun Peng, Zhenyu Zhao, Yiyi Liang, Jiadi Luo, Xiang Wang, and Shaoliang Peng. 2024. Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer.NPJ Precision Oncology8, 1 (...

    2024

  21. [29]

    Ziniu Qian, Kailu Li, Maode Lai, Eric I-Chao Chang, Bingzheng Wei, Yubo Fan, and Yan Xu. 2022. Transformer based multiple instance learning for weakly supervised histopathology image segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Inte...

    2022

  22. [30]

    Shaoyang Qin, Xiaochen Bo, Hongyuan Liu, Zhishuo Zhang, Zhicong Zhao, and Qiang Xia. [n. d.]. Cell therapies and liver organogenesis technologies: Promising strategies for end-stage liver disease.Hepatology([n. d.]), 10–1097

  23. [31]

    Miriam Seoane Santos, Pedro Henriques Abreu, Pedro J García-Laencina, Adélia Simão, and Armando Carvalho. 2015. A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients.Journal of biomedical informatics58 (2015), 49–59

    2015

  24. [32]

    Peter J Scheuer. 1991. Classification of chronic viral hepatitis: a need for reassess- ment.Journal of hepatology13, 3 (1991), 372–374

    1991

  25. [33]

    Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al. 2021. Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in neural information processing systems34 (2021), 2136–2147

    2021

  26. [34]

    Francis E Sharkey, Irina Lytvak, Thomas J Prihoda, K Vincent Speeg, W Kenneth Washburn, and Glenn A Halff. 2011. High-grade microsteatosis and delay in hepatic function after orthotopic liver transplantation.Human pathology42, 9 (2011), 1337–1342

    2011

  27. [35]

    Lulu Sun, Jon N Marsh, Matthew K Matlock, Ling Chen, Joseph P Gaut, Eliza- beth M Brunt, S Joshua Swamidass, and Ta-Chiang Liu. 2020. Deep learning quan- tification of percent steatosis in donor liver biopsy frozen sections.EBioMedicine 60 (2020)

    2020

  28. [36]

    Yoshihisa Takahashi, Erdenetsogt Dungubat, Hiroyuki Kusano, and Toshio Fukusato. 2023. Artificial intelligence and deep learning: New tools for histopatho- logical diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Computational and Structural Biotechn...

    2023

  29. [37]

    Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Efstratios Tsouge- nis, Qitao Huang, Muyan Cai, and Pheng-Ann Heng. 2019. Weakly supervised deep learning for whole slide lung cancer image analysis.IEEE transactions on cybernetics50, 9 (2019), 3950–3962. Conference’17, Ju...

    2019

  30. [38]

    Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Jun- zhou Huang, and Xiao Han. 2022. Transformer-based unsupervised contrastive learning for histopathological image classification.Medical image analysis81 (2022), 102559

    2022

  31. [39]

    Jinxi Xiang and Jun Zhang. 2023. Exploring low-rank property in multiple instance learning for whole slide image classification. InThe Eleventh International Conference on Learning Representations

    2023

  32. [40]

    Ze Xiang, Jiarui Li, Huixuan Zeng, Xiaonan Xiang, Fengqiang Gao, Kai Wang, Xuyong Wei, Shusen Zheng, and Xiao Xu. 2024. Current Understanding of Marginal Grafts in Liver Transplantation.Aging and Disease(2024)

    2024

  33. [41]

    Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, and Junzhou Huang. 2020. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks.Medical image analysis65 (2020), 101789

    2020

  34. [42]

    Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng. 2022. Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In Proceedings of the IEEE/CVF conference on c...

    2022

  35. [43]

    Lin Zhang, Yuntian Deng, Xue Bai, Xiao Wei, Yushuang Ren, Shuang Chen, and Hongxin Deng. 2024. Cell therapy for end-stage liver disease: Current state and clinical challenge.Chinese Medical Journal137, 23 (2024), 2808–2820

    2024

  36. [44]

    Yunlong Zhang, Honglin Li, Yunxuan Sun, Sunyi Zheng, Chenglu Zhu, and Lin Yang. 2024. Attention-challenging multiple instance learning for whole slide image classification. InEuropean Conference on Computer Vision. Springer, 125–143

    2024

  37. [45]

    Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang, Jiarui Sun, Sen Yang, Bjoern Menze, Xinjuan Fan, et al. 2020. Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. InProceedings of the...

    2020

  38. [46]

    backdoor adjustment

    Wenhui Zhu, Xiwen Chen, Peijie Qiu, Aristeidis Sotiras, Abolfazl Razi, and Yalin Wang. 2024. DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification. InEuropean Conference on Computer Vision. Springer, 333–351. DLiPat...

    2024

  39. [2013]

    The impact of hepatic steatosis on hepatic ischemia-reperfusion injury in experimental studies: a systematic review.BioMed Research International2013, 1 (2013), 192029

    2013

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