@misc{dinov2,
  title        = {DINOv2: Learning Robust Visual Features without Supervision},
  author       = {Oquab, Maxime and Darcet, Timothée and Moutakanni, Théo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
  year         = {2024},
  eprint       = {2304.07193},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2304.07193}
}
@inproceedings{abmil,
  title={Attention-based Deep Multiple Instance Learning},
  author={Ilse, Maximilian and Tomczak, Jakub M and Welling, Max},
  booktitle={Proceedings of the 35th International Conference on Machine Learning},
  pages={2127--2136},
  year={2018},
  organization={PMLR}
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Hoebel2026-dy,
  title     = "Graph neural network modeling of spatial tumor-immune
               interactions identifies prognostic cellular niches in non‑small
               cell lung cancer",
  author    = "Hoebel, Katharina V and Lindsay, James R and Altreuter, Jennifer
               and Alessi, Joao V and Weirather, Jason L and Dryg, Ian and
               Giobbie-Hurder, Anita and Li, Zhirou and Yu, Kun-Hsing and Awad,
               Mark M and Rodig, Scott J and Lotter, William",
  journal   = "NPJ Precis. Oncol.",
  publisher = "Springer Science and Business Media LLC",
  month     =  mar,
  year      =  2026,
  language  = "en"
}


@inproceedings{cpathrsa,
  title={Comparing Computational Pathology Foundation Models using
Representational Similarity Analysis},
  author={Mishra, Vaibhav and Lotter, William},
  booktitle={Proceedings of the 5th Machine Learning for Health (ML4H) Symposium},
  year={2025},
  organization={PMLR}
}

@ARTICLE{Schmid2024-gb,
  title     = "Overall survival with pembrolizumab in early-stage
               triple-negative breast cancer",
  author    = "Schmid, Peter and Cortes, Javier and Dent, Rebecca and McArthur,
               Heather and Pusztai, Lajos and K{\"u}mmel, Sherko and Denkert,
               Carsten and Park, Yeon Hee and Hui, Rina and Harbeck, Nadia and
               Takahashi, Masato and Im, Seock-Ah and Untch, Michael and
               Fasching, Peter A and Mouret-Reynier, Marie-Ange and Foukakis,
               Theodoros and Ferreira, Marta and Cardoso, Fatima and Zhou, Xuan
               and Karantza, Vassiliki and Tryfonidis, Konstantinos and Aktan,
               Gursel and O'Shaughnessy, Joyce and {KEYNOTE-522 Investigators}",
  abstract  = "BACKGROUND: In patients with early-stage triple-negative breast
               cancer, the phase 3 KEYNOTE-522 trial showed significant
               improvements in pathological complete response and event-free
               survival with the addition of pembrolizumab to
               platinum-containing chemotherapy. Here we report the final
               results for overall survival. METHODS: We randomly assigned, in
               a 2:1 ratio, patients with previously untreated stage II or III
               triple-negative breast cancer to receive neoadjuvant therapy
               with four cycles of pembrolizumab (at a dose of 200 mg) or
               placebo every 3 weeks plus paclitaxel and carboplatin, followed
               by four cycles of pembrolizumab or placebo plus
               doxorubicin-cyclophosphamide or epirubicin-cyclophosphamide.
               After definitive surgery, patients received adjuvant
               pembrolizumab (pembrolizumab-chemotherapy group) or placebo
               (placebo-chemotherapy group) every 3 weeks for up to nine
               cycles. The primary end points were pathological complete
               response and event-free survival. Overall survival was a
               secondary end point. RESULTS: Of the 1174 patients who underwent
               randomization, 784 were assigned to the
               pembrolizumab-chemotherapy group and 390 to the
               placebo-chemotherapy group. At the data-cutoff date (March 22,
               2024), the median follow-up was 75.1 months (range, 65.9 to
               84.0). The estimated overall survival at 60 months was 86.6\%
               (95\% confidence interval [CI], 84.0 to 88.8) in the
               pembrolizumab-chemotherapy group, as compared with 81.7\% (95\%
               CI, 77.5 to 85.2) in the placebo-chemotherapy group (P = 0.002).
               Adverse events were consistent with the established safety
               profiles of pembrolizumab and chemotherapy. CONCLUSIONS:
               Neoadjuvant pembrolizumab plus chemotherapy followed by adjuvant
               pembrolizumab resulted in a significant improvement, as compared
               with neoadjuvant chemotherapy alone, in overall survival among
               patients with early-stage triple-negative breast cancer. (Funded
               by Merck Sharp and Dohme, a subsidiary of Merck [Rahway, NJ];
               KEYNOTE-522 ClinicalTrials.gov number, NCT03036488.).",
  journal   = "N. Engl. J. Med.",
  publisher = "Massachusetts Medical Society",
  volume    =  391,
  number    =  21,
  pages     = "1981--1991",
  month     =  nov,
  year      =  2024,
  copyright = "http://www.nejmgroup.org/legal/terms-of-use.htm",
  language  = "en"
}


@ARTICLE{Abdou2022-di,
  title     = "Immunotherapy in triple negative breast cancer: beyond
               checkpoint inhibitors",
  author    = "Abdou, Yara and Goudarzi, Atta and Yu, Jia Xin and Upadhaya,
               Samik and Vincent, Benjamin and Carey, Lisa A",
  abstract  = "The development of immunotherapy agents has revolutionized the
               field of oncology. The only FDA-approved immunotherapeutic
               approach in breast cancer consists of immune checkpoint
               inhibitors, yet several novel immune-modulatory strategies are
               being actively studied and appear promising. Innovative
               immunotherapeutic strategies are urgently needed in triple
               negative breast cancer (TNBC), a subtype of breast cancer known
               for its poor prognosis and its resistance to conventional
               treatments. TNBC is more primed to respond to immunotherapy
               given the presence of more tumor infiltrating lymphocytes,
               higher PD-L1 expression, and higher tumor mutation burden
               relative to the other breast cancer subtypes, and therefore,
               immuno-oncology represents a key area of promise for TNBC
               research. The aim of this review is to highlight current data
               and ongoing efforts to establish the safety and efficacy of
               immunotherapeutic approaches beyond checkpoint inhibitors in
               TNBC.",
  journal   = "NPJ Breast Cancer",
  publisher = "Springer Science and Business Media LLC",
  volume    =  8,
  number    =  1,
  pages     = "121",
  month     =  nov,
  year      =  2022,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}


@ARTICLE{naturemethods,
  title    = "Method of the Year 2024: spatial proteomics",
  author = "Nat. Methods",
  journal  = "Nat. Methods",
  volume   =  21,
  number   =  12,
  pages    = "2195--2196",
  month    =  dec,
  year     =  2024,
  language = "en"
}

@ARTICLE{Zadeh2021-ht,
  title     = "Bias in cross-entropy-based training of deep survival networks",
  author    = "Zadeh, Shekoufeh Gorgi and Schmid, Matthias",
  abstract  = "Over the last years, utilizing deep learning for the analysis of
               survival data has become attractive to many researchers. This
               has led to the advent of numerous network architectures for the
               prediction of possibly censored time-to-event variables. Unlike
               networks for cross-sectional data (used e.g., in
               classification), deep survival networks require the
               specification of a suitably defined loss function that
               incorporates typical characteristics of survival data such as
               censoring and time-dependent features. Here, we provide an
               in-depth analysis of the cross-entropy loss function, which is a
               popular loss function for training deep survival networks. For
               each time point t, the cross-entropy loss is defined in terms of
               a binary outcome with levels ``event at or before t'' and
               ``event after t''. Using both theoretical and empirical
               approaches, we show that this definition may result in a high
               prediction error and a heavy bias in the predicted survival
               probabilities. To overcome this problem, we analyze an
               alternative loss function that is derived from the negative
               log-likelihood function of a discrete time-to-event model. We
               show that replacing the cross-entropy loss by the negative
               log-likelihood loss results in much better calibrated prediction
               rules and also in an improved discriminatory power, as measured
               by the concordance index.",
  journal   = "IEEE Trans. Pattern Anal. Mach. Intell.",
  publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
  volume    =  43,
  number    =  9,
  pages     = "3126--3137",
  month     =  sep,
  year      =  2021,
  language  = "en"
}


@misc{grl,
      title={Unsupervised Domain Adaptation by Backpropagation}, 
      author={Yaroslav Ganin and Victor Lempitsky},
      year={2015},
      eprint={1409.7495},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/1409.7495}, 
}

@ARTICLE{leiden,
  title     = "From Louvain to Leiden: guaranteeing well-connected communities",
  author    = "Traag, V A and Waltman, L and van Eck, N J",
  abstract  = "Community detection is often used to understand the structure of
               large and complex networks. One of the most popular algorithms
               for uncovering community structure is the so-called Louvain
               algorithm. We show that this algorithm has a major defect that
               largely went unnoticed until now: the Louvain algorithm may
               yield arbitrarily badly connected communities. In the worst
               case, communities may even be disconnected, especially when
               running the algorithm iteratively. In our experimental analysis,
               we observe that up to 25\% of the communities are badly
               connected and up to 16\% are disconnected. To address this
               problem, we introduce the Leiden algorithm. We prove that the
               Leiden algorithm yields communities that are guaranteed to be
               connected. In addition, we prove that, when the Leiden algorithm
               is applied iteratively, it converges to a partition in which all
               subsets of all communities are locally optimally assigned.
               Furthermore, by relying on a fast local move approach, the
               Leiden algorithm runs faster than the Louvain algorithm. We
               demonstrate the performance of the Leiden algorithm for several
               benchmark and real-world networks. We find that the Leiden
               algorithm is faster than the Louvain algorithm and uncovers
               better partitions, in addition to providing explicit guarantees.",
  journal   = "Sci. Rep.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  9,
  number    =  1,
  pages     = "5233",
  month     =  mar,
  year      =  2019,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@article {Lindsay2025.03.05.641733,
  title     = "Pan-cancer spatial characterization of key immune biomarkers in
               the tumor microenvironment",
  author    = "Lindsay, James R and Altreuter, Jennifer and Alessi, Joao V and
               Weirather, Jason L and Giobbie-Hurder, Anita and Dryg, Ian and
               Hoebel, Katharina and Sharma, Bijaya and Felt, Kristen and Hodi,
               F Stephen and Lindeman, Neal and Sholl, Lynette M and Cerami,
               Ethan and Nowak, Jonathan A and Awad, Mark M and Rodig, Scott J
               and Lotter, William",
  abstract  = "Deciphering the composition and spatial organization of the
               tumor immune microenvironment (TIME) is key to understanding
               cancer progression and treatment response. Spatial biology
               techniques such as multiplex immunofluorescence (mIF) offer
               detailed insights but are often limited to retrospective studies
               of individual cancer types. Here, we provide a pan-cancer
               spatial characterization of key biomarkers (CD8, FOXP3, PD-1,
               and PD-L1) using an mIF assay performed prospectively in a
               clinical setting on 2,019 tumors across 14 major cancer types.
               By integrating interpretable compositional and spatial metrics,
               we identify patterns of TIME variation conserved across cancer
               types and stages. We assess associations between these TIME
               spatial factors and tumor, genomic, and clinical features, with
               results extending prior findings and uncovering new links. To
               accompany the analysis, we provide a curated database of the
               39.4 million spatially resolved cells. Altogether, our findings
               and database offer pan-cancer insights of the TIME to advance
               spatial biology and cancer immunology.",
  journal   = "Cell Rep. Med.",
  publisher = "Elsevier BV",
  number    =  102418,
  pages     = "102418",
  month     =  oct,
  year      =  2025,
  keywords  = "immuno-oncology; multiplex immunofluorescence; spatial
               proteomics; tumor immune microenvironment",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}

@article{adamw,
  author       = {Ilya Loshchilov and
                  Frank Hutter},
  title        = {Fixing Weight Decay Regularization in Adam},
  journal      = {CoRR},
  volume       = {abs/1711.05101},
  year         = {2017},
  url          = {http://arxiv.org/abs/1711.05101},
  eprinttype    = {arXiv},
  eprint       = {1711.05101},
  timestamp    = {Mon, 13 Aug 2018 16:48:18 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1711-05101.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{Alessi2024.12.15.24318012,
  title     = "Immunoprofiling at an institutional scale reveals that high
               numbers of intratumoral {CD8+} and {PD-1+} cells predict
               superior patient survival across major cancer types independent
               of major risk factors",
  author    = "Alessi, Joao V and Lindsay, James R and Giobbie-Hurder, Anita
               and Sharma, Bijaya and Felt, Kristen and Kumari, Priti and
               Mazor, Tali and Cerami, Ethan and Lotter, William and Altreuter,
               Jennifer and Weirather, Jason and Dryg, Ian and Hoebel,
               Katharina and Manos, Michael and Adib, Elio and Curtis, Jennifer
               D and Ricciuti, Biagio and Di Federico, Alessandro and Ghandour,
               Fatme and Saad, Eddy and Wang, Xin-An and Pecci, Federica and
               Holovatska, Marta and Gandhi, Malini M and Hughes, Melissa E and
               O'Meara, Tess A and Chan, Sabrina J and Pfaff, Kathleen and
               Konstantinopoulos, Panagiotis A and Hodi, F Stephan and Shipp,
               Margaret A and Signoretti, Sabina and Choueiri, Toni and Wei,
               Xiao X and Santagata, Sandro and Hanna, Glenn J and Lin, Nancy U
               and Tolaney, Sara M and Liu, Joyce and Sorger, Peter K and
               Lindeman, Neal and Sholl, Lynette M and Nowak, Jonathan A and
               Barbie, David and Awad, Mark M and Johnson, Bruce E and Rodig,
               Scott J",
  abstract  = "PURPOSE: Retrospective studies have found associations between
               the number of intratumoral immune cells and patient outcomes for
               specific cancers treated with targeted therapies. However, the
               clinical value of routinely quantifying intratumoral immune
               biomarkers using a digital pathology platform in the pan-cancer
               setting within an active clinical laboratory has not been
               established. METHODS: We developed ImmunoProfile, a daily
               clinical workflow that integrates automated multiplex
               immunofluorescence tissue staining, digital slide imaging, and
               machine learning-assisted scoring to quantify intratumoral CD8+,
               PD-1+, CD8+PD-1+, and FOXP3+ immune cells and PD-L1 expression
               in formalin-fixed, paraffin-embedded tissue samples in a
               standardized and reproducible manner. We prospectively applied
               ImmunoProfile to biopsies collected from 2,023 unselected
               patients with cancer over a 3-year period in the clinical
               laboratory and correlated the results with patient survival.
               RESULTS: In the pan-cancer cohort, patients with high numbers of
               intratumoral CD8+ or PD-1+ cells in had significantly lower
               risks of death compared with those with low numbers (CD8+: high
               v low hazard ratio [HR], 0.62 [95\% CI, 0.48 to 0.81], Wald P =
               .002; PD-1+: high v low HR, 0.65 [95\% CI, 0.51 to 0.83]; P =
               .0009) after adjusting for risk factors, including cancer type.
               In subset analyses, patients with high numbers of intratumoral
               CD8+, PD-1+, and/or CD8+PD-1+ cells showed lower risks of death
               from non-small cell lung, colorectal, breast, esophagogastric,
               head and neck, pancreatic, and ovarian cancers after considering
               clinical risk factors, including American Joint Committee on
               Cancer stage, and despite varying therapies (all P < .05).
               CONCLUSION: Routinely quantifying intratumoral CD8+ and PD-1+
               cells with a clinically validated digital pathology platform
               predicts patient survival across major cancer types, independent
               of clinical stage and despite diverse treatment regimens.",
  journal   = "JCO Precis. Oncol.",
  publisher = "American Society of Clinical Oncology (ASCO)",
  volume    =  9,
  number    =  9,
  pages     = "e2500240",
  month     =  sep,
  year      =  2025,
  language  = "en"
}



@article{cancerstats,
author = {Bray, Freddie and Laversanne, Mathieu and Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L. and Soerjomataram, Isabelle and Jemal, Ahmedin},
title = {Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
journal = {CA: A Cancer Journal for Clinicians},
volume = {74},
number = {3},
pages = {229-263},
keywords = {cancer burden, cancer control, epidemiology, incidence, mortality},
doi = {https://doi.org/10.3322/caac.21834},
url = {https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21834},
eprint = {https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21834},
abstract = {Abstract This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC). There were close to 20 million new cases of cancer in the year 2022 (including nonmelanoma skin cancers [NMSCs]) alongside 9.7 million deaths from cancer (including NMSC). The estimates suggest that approximately one in five men or women develop cancer in a lifetime, whereas around one in nine men and one in 12 women die from it. Lung cancer was the most frequently diagnosed cancer in 2022, responsible for almost 2.5 million new cases, or one in eight cancers worldwide (12.4\% of all cancers globally), followed by cancers of the female breast (11.6\%), colorectum (9.6\%), prostate (7.3\%), and stomach (4.9\%). Lung cancer was also the leading cause of cancer death, with an estimated 1.8 million deaths (18.7\%), followed by colorectal (9.3\%), liver (7.8\%), female breast (6.9\%), and stomach (6.8\%) cancers. Breast cancer and lung cancer were the most frequent cancers in women and men, respectively (both cases and deaths). Incidence rates (including NMSC) varied from four-fold to five-fold across world regions, from over 500 in Australia/New Zealand (507.9 per 100,000) to under 100 in Western Africa (97.1 per 100,000) among men, and from over 400 in Australia/New Zealand (410.5 per 100,000) to close to 100 in South-Central Asia (103.3 per 100,000) among women. The authors examine the geographic variability across 20 world regions for the 10 leading cancer types, discussing recent trends, the underlying determinants, and the prospects for global cancer prevention and control. With demographics-based predictions indicating that the number of new cases of cancer will reach 35 million by 2050, investments in prevention, including the targeting of key risk factors for cancer (including smoking, overweight and obesity, and infection), could avert millions of future cancer diagnoses and save many lives worldwide, bringing huge economic as well as societal dividends to countries over the forthcoming decades.},
year = {2024}
}



@ARTICLE{Wu2022-lx,
  title     = "Graph deep learning for the characterization of tumour
               microenvironments from spatial protein profiles in tissue
               specimens",
  author    = "Wu, Zhenqin and Trevino, Alexandro E and Wu, Eric and Swanson,
               Kyle and Kim, Honesty J and D'Angio, H Blaize and Preska, Ryan
               and Charville, Gregory W and Dalerba, Piero D and Egloff, Ann
               Marie and Uppaluri, Ravindra and Duvvuri, Umamaheswar and Mayer,
               Aaron T and Zou, James",
  abstract  = "Multiplexed immunofluorescence imaging allows the
               multidimensional molecular profiling of cellular environments at
               subcellular resolution. However, identifying and characterizing
               disease-relevant microenvironments from these rich datasets is
               challenging. Here we show that a graph neural network that
               leverages spatial protein profiles in tissue specimens to model
               tumour microenvironments as local subgraphs captures distinctive
               cellular interactions associated with differential clinical
               outcomes. We applied this spatial cellular-graph strategy to
               specimens of human head-and-neck and colorectal cancers assayed
               with 40-plex immunofluorescence imaging to identify spatial
               motifs associated with cancer recurrence and with patient
               survival after treatment. The graph deep learning model was
               substantially more accurate in predicting patient outcomes than
               deep learning approaches that model spatial data on the basis of
               the local composition of cell types, and it generated insights
               into the effect of the spatial compartmentalization of tumour
               cells and granulocytes on patient prognosis. Local graphs may
               also aid in the analysis of disease-relevant motifs in histology
               samples characterized via spatial transcriptomics and other
               -omics techniques.",
  journal   = "Nat. Biomed. Eng.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  6,
  number    =  12,
  pages     = "1435--1448",
  month     =  dec,
  year      =  2022,
  copyright = "https://www.springernature.com/gp/researchers/text-and-data-mining",
  language  = "en"
}


@ARTICLE{De_Souza2024-oy,
  title     = "Multiplex protein imaging in tumour biology",
  author    = "de Souza, Natalie and Zhao, Shan and Bodenmiller, Bernd",
  abstract  = "Tissue imaging has become much more colourful in the past
               decade. Advances in both experimental and analytical methods now
               make it possible to image protein markers in tissue samples in
               high multiplex. The ability to routinely image 40-50 markers
               simultaneously, at single-cell or subcellular resolution, has
               opened up new vistas in the study of tumour biology. Cellular
               phenotypes, interaction, communication and spatial organization
               have become amenable to molecular-level analysis, and
               application to patient cohorts has identified clinically
               relevant cellular and tissue features in several cancer types.
               Here, we review the use of multiplex protein imaging methods to
               study tumour biology, discuss ongoing attempts to combine these
               approaches with other forms of spatial omics, and highlight
               challenges in the field.",
  journal   = "Nat. Rev. Cancer",
  publisher = "Springer Science and Business Media LLC",
  volume    =  24,
  number    =  3,
  pages     = "171--191",
  month     =  mar,
  year      =  2024,
  copyright = "https://www.springernature.com/gp/researchers/text-and-data-mining",
  language  = "en"
}

@ARTICLE{Vorontsov2024-zw,
  title     = "A foundation model for clinical-grade computational pathology
               and rare cancers detection",
  author    = "Vorontsov, Eugene and Bozkurt, Alican and Casson, Adam and
               Shaikovski, George and Zelechowski, Michal and Severson, Kristen
               and Zimmermann, Eric and Hall, James and Tenenholtz, Neil and
               Fusi, Nicolo and Yang, Ellen and Mathieu, Philippe and van Eck,
               Alexander and Lee, Donghun and Viret, Julian and Robert, Eric
               and Wang, Yi Kan and Kunz, Jeremy D and Lee, Matthew C H and
               Bernhard, Jan H and Godrich, Ran A and Oakley, Gerard and
               Millar, Ewan and Hanna, Matthew and Wen, Hannah and Retamero,
               Juan A and Moye, William A and Yousfi, Razik and Kanan,
               Christopher and Klimstra, David S and Rothrock, Brandon and Liu,
               Siqi and Fuchs, Thomas J",
  abstract  = "The analysis of histopathology images with artificial
               intelligence aims to enable clinical decision support systems
               and precision medicine. The success of such applications depends
               on the ability to model the diverse patterns observed in
               pathology images. To this end, we present Virchow, the largest
               foundation model for computational pathology to date. In
               addition to the evaluation of biomarker prediction and cell
               identification, we demonstrate that a large foundation model
               enables pan-cancer detection, achieving 0.95 specimen-level area
               under the (receiver operating characteristic) curve across nine
               common and seven rare cancers. Furthermore, we show that with
               less training data, the pan-cancer detector built on Virchow can
               achieve similar performance to tissue-specific clinical-grade
               models in production and outperform them on some rare variants
               of cancer. Virchow's performance gains highlight the value of a
               foundation model and open possibilities for many high-impact
               applications with limited amounts of labeled training data.",
  journal   = "Nat. Med.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  30,
  number    =  10,
  pages     = "2924--2935",
  month     =  oct,
  year      =  2024,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@ARTICLE{Xu2024-pf,
  title     = "A whole-slide foundation model for digital pathology from
               real-world data",
  author    = "Xu, Hanwen and Usuyama, Naoto and Bagga, Jaspreet and Zhang,
               Sheng and Rao, Rajesh and Naumann, Tristan and Wong, Cliff and
               Gero, Zelalem and Gonz{\'a}lez, Javier and Gu, Yu and Xu, Yanbo
               and Wei, Mu and Wang, Wenhui and Ma, Shuming and Wei, Furu and
               Yang, Jianwei and Li, Chunyuan and Gao, Jianfeng and Rosemon,
               Jaylen and Bower, Tucker and Lee, Soohee and Weerasinghe,
               Roshanthi and Wright, Bill J and Robicsek, Ari and Piening,
               Brian and Bifulco, Carlo and Wang, Sheng and Poon, Hoifung",
  abstract  = "Digital pathology poses unique computational challenges, as a
               standard gigapixel slide may comprise tens of thousands of image
               tiles1-3. Prior models have often resorted to subsampling a
               small portion of tiles for each slide, thus missing the
               important slide-level context4. Here we present Prov-GigaPath, a
               whole-slide pathology foundation model pretrained on 1.3 billion
               256 $\times$ 256 pathology image tiles in 171,189 whole slides
               from Providence, a large US health network comprising 28 cancer
               centres. The slides originated from more than 30,000 patients
               covering 31 major tissue types. To pretrain Prov-GigaPath, we
               propose GigaPath, a novel vision transformer architecture for
               pretraining gigapixel pathology slides. To scale GigaPath for
               slide-level learning with tens of thousands of image tiles,
               GigaPath adapts the newly developed LongNet5 method to digital
               pathology. To evaluate Prov-GigaPath, we construct a digital
               pathology benchmark comprising 9 cancer subtyping tasks and 17
               pathomics tasks, using both Providence and TCGA data6. With
               large-scale pretraining and ultra-large-context modelling,
               Prov-GigaPath attains state-of-the-art performance on 25 out of
               26 tasks, with significant improvement over the second-best
               method on 18 tasks. We further demonstrate the potential of
               Prov-GigaPath on vision-language pretraining for pathology7,8 by
               incorporating the pathology reports. In sum, Prov-GigaPath is an
               open-weight foundation model that achieves state-of-the-art
               performance on various digital pathology tasks, demonstrating
               the importance of real-world data and whole-slide modelling.",
  journal   = "Nature",
  publisher = "Springer Science and Business Media LLC",
  volume    =  630,
  number    =  8015,
  pages     = "181--188",
  month     =  jun,
  year      =  2024,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}


@inproceedings{dino,
  title={Emerging Properties in Self-Supervised Vision Transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e  and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle={Proceedings of the International Conference on Computer Vision (ICCV)},
  year={2021}
}
@misc{dejong2025,
      title={Current Pathology Foundation Models are unrobust to Medical Center Differences}, 
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