State-of-the-Art Survey: AI/Machine Learning for Digital Pathology

Call for Papers (due to September, 30, 2019)


Open Call for Papers due to September, 30, 2019

Edited by Andreas Holzinger & Heimo Müller

Following the huge success of LNAI 9605 which has 78k+ downloads so far, we are collecting papers on the hot topic of AI/Machine Learning cross-domain bridging with Digital Pathology for a Springer Lecture Notes on Artificial Intellience (LNAI).

Artificial Intelligence (AI) and Machine Learning (ML)  in digital pathology is very promising, e.g. in specific cases machine learning approaches, particularly Deep Learning (DL), even exceeds human performance (see for the difference of AI/ML/DL). However, in the context of medicine it is important for a human expert to validate the outcome and/or to interact with the AI. Current AI models lack an explicit explanation component that allows a human to understand the results. There is a need for transparency and thus traceability of such solutions to make them usable for medical decision support. The combined use of human intelligence and artificial intelligence for context understanding should bring important insights and new methodological solutions.

Machine Learning (ML) requires big training data sets that well cover the spectrum of a variety of human diseases in different organ systems. Data sets have to meet quality- and regulatory criteria and must be well annotated for machine learning at patient-, sample- and image-level. Here biobanks play a central and future role providing large collections of high-quality well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.



Papers are sought cross-domain from Artificial Intelligence/Machine Learning for Digital Pathology, including but not limited to (list not yet complete, in no order):

  • Weakly supervised learning
  • Multiple Instance Learning
  • Multi-Classifier Systems
  • Transparent Machine Learning
  • Interpretable Machine Learning
  • Explainable AI (exAI)
  • Explainability and Causability
  • Ethical, Social and Legal Aspects of AI
  • Transfer Learning
  • Privacy-Preserving Machine Learning
  • Performance measures
  • Conditional random fields
  • Deep Learning approaches
  • Ontologies and Machine Learning
  • Interactive Machine Learning
  • Identification of diagnostic, prognostic and theragnostic biomarkers
  • Data preprocessing, Data mapping, Data fusion, Data integration, Data mapping
  • Data provenance and data curation
  • Biobank-sample and data quality
  • etc.

Each paper shall have a special structure (see below).

Papers which deal with fundamental research questions and theoretical aspects in machine learning/digital pathology are very welcome.


Quality needs time – and we want to ensure the highest possible quality, to provide a clear benefit to our potential readers; this needs careful reviewing and revision phases so we aim to complete this Volume by the end of 2019.

Please send your paper proposal directly to the editors. After invitation for submission prepare your paper following the Springer llncs2e style (llncs.cls, splncs.bst),  the template can be found comfortably on Overleaf:

There is no definite page limit – but the ususal chapters are between 10 and 20 pages. However, in any case, please produce even pages to ensure smooth page breaks, e.g. 10, 12, 14, 16, 18, 20 pages.

NOTE: This State-of-the-art volume shall bring exclusive benefits for the readers and shall be of archival value on the desks and benches of both scientists, industrial practictioners, teachers and students (it is not necessary to produce rocket-science papers or Nobel-prize papers, which only a few people on this planet understand). Moreover, it shall be useful for fostering joint projects at national, European, and international level.


For this purpose each chapter is required to follow a specific structure:

    A very short and concise introduction and motivation on why and how this chapter is important and for whom;
    The used terms shall be defined at first, so that a common understanding is guaranteed;
    This is the main part and may be divided into traditional subchapters accordingly;
    Here you should highlight potential known obstacles so that others can avoid to make these errors in advance;
    This shall outline future research avenues, hot topics and research challenges of further interest;

You can refer to a successful sample Volume and look there for sample papers:
Holzinger, A. &  Jurisica I. (2014). Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges, LNCS 8401, Berlin Heidelberg: Springer, 2014.

Here you find the general Springer LNCS information page


Please submit your paper directly to one of the editors: or


Your paper will be assigned to three reviewers from our international advisory and review board, so that you will receive useful feedback on how to further improve your paper. You will get notified in due course to prepare the final version. For full transparency of the review process, you can find the review template here (scroll down to the middle of the page):



To fully understand the intention of a state-of-the-art Volume you can read a draft editorial here:
Holzinger (2016) DRAFT-Editorial-Integrative-Machine-Learning-for-Health  (pdf, 564kB)

Please revise your paper according to the reviewer requests and send the following three items
directly to the editors:
1) Your paper as pdf (please ensure even page numbers, e.g. … 14, 16, 18, 20, 22 … pages)
2) Your source files (LaTeX preferred – pack all source files in one single zip-folder)
3) The signed letter of consent as pdf scan –
please download the form here: LNCS-Springer-Letter-of-Consent-LNCS-SOTA-MLHealth   (pdf, 68kB)


Your files will be carefully checked and send into production. Authors will be contacted for checking the page proofs directly by the Springer production team.  The Volume is targeted to be finalized and printed end of the year 2019 – quality needs time. As a gratitude you will receive one copy of the printed volume fresh from the press.

International Scientific Advisory Board

(in alphabetic order, not yet complete, status as of 05.09.2019, 07:00 MDT, UTC-6)

Peter BANKHEAD, Centre for Genomic and Experimental Medicine, Division of Pathology, University of Edinburgh, United Kingdom

Jaesik CHOI, Explainable Artificial Intelligence Center, Statistical Artificial Intlelligence Lab, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea

Toby CORNISH,  Department of Pathology, School of Medicine, Anschutz Medical Campus, University of Colorado, Aurora (CO), United States of America

Thomas J. FUCHS, Fuchs Lab, Memorial Sloan Kettering Cancer Center, New York (NY), United States of America

David Andrew GUTMAN, Gutman Lab, Department of Biomedical Informatics, Emory University School of Medicine, Atlanta (GA), United States of America

Anant MADABHUSHI, Center for Computational Imaging & Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland (OH), United States of America

Craig MERMEL, Digital Pathology, Google AI, Mountain View, Santa Clara (CA), United States of America

Jose M. ORAMAS, Center for Processing Speech and Images, KU Leuven, Belgium

Liron PANTANOWITZ, Department of Pathology, University of Pittsburgh Schools of Health Sciences (UPMC), Pittsburgh, United States of America

Christin SEIFERT, Data Science group, University of Twente, Enschede, The Netherlands

Klaus-Robert MÜLLER, Machine Learning Group, TU Berlin, Germany and Korea University, Seoul, South Korea

Nasir M. RAJPOOT, Tissue Image Analytics (TIA) Lab, Department of Computer Science, University of Warwick, UK

Kurt ZATLOUKAL, Diagnostic and Research Center for Molecular BioMedicine, Medical University Graz, Austria

Jianlong ZHOU, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia