A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics
Conditions: Hematologic Malignancy, Leukemia, Minimal Residual Disease, Lymphoma, Blood Cancer
Sponsor: Munich Leukemia Laboratory

 
 
COVER PAGE 
BETTER LEUKEMIA DIAGNOSTICS THROUGH AI (BELUGA) 
 
 
Long title: A Case-Control Study To Determine The Suitability Of Artificial Intelligence 
For Leukemia Diagnostics 
 
Submission ID: MLL_001 
 
Sponsor of the trial: Prof. Dr. Dr. Torsten Haferlach, Munich Leukemia Laboratory (MLL) – 
MLL Münchner Leukämielabor GmbH 
 
Address: MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 MUNICH 
(GERMANY) 
 
Document Date (Date of initial Submission): 6TH OF JULY 2020 (07—06—2020) 
Date of Submission of the revised Document: 9th OF JULY 2020 (07—09—2020) 
 
NCT Number: NCT ID not yet assigned 
 
Submitting investigator: Adam Wahida, MD 
Contact Email: adam.wahida@mll.com 
 

The information provided in this document is confidential and proprietary to the MLL Munich Leukemia Laboratory. The information 
in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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Protocol Title: A Case-Control Study To Determine The 
Suitability 
Of 
Artificial 
Intelligence 
For 
Leukemia 
Diagnostics 
 
Running Title: BETTER LEUKEMIA DIAGNOSTICS THROUGH AI (BELUGA) 
Principal Investigator (PI): Prof. Dr. med. Dr. phil. Torsten Haferlach (TH) 
Telephone: +49 (0)89 99017-100 
Fax: +49 (0)89 99017-109 
E-Mail: torsten.haferlach@mll.com 
Co-Investigator(s): Prof. Dr. med. Wolfgang Kern (WK), Adam Wahida (AW) 
Sponsor: Prof. Dr. Dr. Torsten Haferlach, Munich Leukemia Laboratory (MLL) – MLL Münchner 
Leukämielabor GmbH  
Address: MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 MUNICH 
(GERMANY) 
Telephone: +49 (0)89 99017-100  
GCP Statement: BELUGA will be performed in compliance with the good clinical practice 
directive from the European Union (2005/28/EC) 
Funding: MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 MUNICH 
(GERMANY) 

The information provided in this document is confidential and proprietary to the MLL Munich Leukemia Laboratory. The information 
in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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SUMMARY 
To the best of our knowledge, BELUGA will be the first prospective trial investigating the 
usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an 
unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and 
digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be 
compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter 
MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry 
datapoints will be prospectively used to train an AI-based deep neuronal network for correct 
categorization. Subsequently, the superiority will be challenged for the primary endpoints: 
sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The 
secondary endpoints will compare the consequences regarding further diagnostic work-up and, 
thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA 
will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting. 
 
Type of Research: observational study, diagnostic study, hematology, leukemia 
Intervention: none 
 
 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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APPROVAL 
This study will be conducted with the utmost respect for individual patients in accordance with the 
requirements of this diagnostic trial protocol and especially per the following: 
§ Good clinical practice directive (European Union) (2005/28/EC) 
§ The ethical principles according to the Declaration of Helsinki 
§ International Conference on Harmonization (ICH) E6 Good Clinical Practice: consolidated 
guideline 
§ Guidelines for Good Clinical Practice (Deutsche Forschungsgemeinschaft DFG) 
§ All other applicable laws and regulations, including, data privacy laws, clinical trial 
disclosure laws, and regulations 
 
 

The information provided in this document is confidential and proprietary to the MLL Munich Leukemia Laboratory. The information 
in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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STUDY SUMMARY 
Protocol Title 
A Case-Control Study To Determine The Suitability Of 
Artificial Intelligence For Leukemia Diagnostics 
 
Running Title 
BEtter LeUkemia diaGnostics through AI (BELUGA) 
 
Main Aim 
 
Prospectively determine the superiority of pattern recognition 
in the classification of hematological disorders 
 
Coordinating Investigator 
 
Prof. Dr. med. Dr. phil. Torsten Haferlach 
Telephone: +49 (0)89 99017-100 
Fax: +49 (0)89 99017-109 
E-Mail: torsten.haferlach@mll.com 
 
Primary Objective 
 
Assessing sensitivity and specificity of pattern recognition in 
performing hematologic diagnostics in comparison to current 
state-of-the-art algorithms 
 
Secondary Objective 
§ comparison of clinical consequences 
§ predictive diagnostic value 
§ turn-around-time  
§ enumerate entity-specific benchmarks (e.g., blast 
count) 
 
Explorative Objectives 
 
Determine the relationship between AI and non-AI supported 
diagnostic tools for hematologic diagnostics 
 
Study Center 
 
Munich Leukemia Laboratory (MLL) 
Trial Site 
 
Munich Leukemia Laboratory (MLL) 
Number of Subjects 
 
The estimated number of patients throughout the study 
duration: 25,000 
 
Diagnosis and Main 
Inclusion Criteria 
 
Peripheral blood smears for morphology and/or peripheral 
blood or bone marrow for immunophenotyping from all 
hematological malignancies recognized by current WHO 
classification (Swerdlow et al. 2017) 
 
Duration of Study 
 
The study will be conducted throughout twelve (12) months 
from the 1st of August 2020 until the 31st of July 2021. 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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TABLE OF CONTENTS 
INTRODUCTION ................................................................................................................................................... - 7 - 
BACKGROUND AND RATIONALE ............................................................................................................................. - 7 - 
OBJECTIVES ............................................................................................................................................................ - 8 - 
STUDY DESIGN ................................................................................................................................................... - 10 - 
TYPE OF STUDY .................................................................................................................................................... - 10 - 
DURATION OF STUDY ........................................................................................................................................... - 11 - 
QUALITY CONTROL .............................................................................................................................................. - 11 - 
PRIMARY STUDY ENDPOINTS ............................................................................................................................... - 12 - 
Endpoint assessment ...................................................................................................................................... - 12 - 
Primary endpoint ........................................................................................................................................... - 12 - 
SECONDARY ENDPOINTS ...................................................................................................................................... - 13 - 
SUBJECT SELECTION AND WITHDRAWAL .............................................................................................. - 14 - 
NUMBER OF SUBJECTS ......................................................................................................................................... - 14 - 
GENDER AND AGE ................................................................................................................................................ - 14 - 
INCLUSION CRITERIA ........................................................................................................................................... - 14 - 
EXCLUSION CRITERIA .......................................................................................................................................... - 14 - 
SUBJECT IDENTIFICATION & RECRUITMENT ........................................................................................................ - 15 - 
LOCATION ............................................................................................................................................................ - 15 - 
STATISTICAL PLAN .......................................................................................................................................... - 16 - 
SAMPLE SIZE ........................................................................................................................................................ - 16 - 
STATISTICAL METHODS ....................................................................................................................................... - 16 - 
RISKS AND BENEFITS ...................................................................................................................................... - 16 - 
POTENTIAL DIRECT BENEFITS TO SUBJECT .......................................................................................................... - 16 - 
DATA HANDLING AND RECORD ................................................................................................................... - 17 - 
STUDY MONITORING, AUDITING, AND INSPECTION ............................................................................ - 17 - 
FINANCIAL CONSIDERATIONS ..................................................................................................................... - 17 - 
CONFLICT OF INTEREST ................................................................................................................................ - 18 - 
PUBLICATION PLAN ......................................................................................................................................... - 18 - 
ARCHIVING ......................................................................................................................................................... - 19 - 
TRIAL MASTER FILE ............................................................................................................................................. - 19 - 
REFERENCES ...................................................................................................................................................... - 20 - 
 
 
 

The information provided in this document is confidential and proprietary to the MLL Munich Leukemia Laboratory. The information 
in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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INTRODUCTION 
Background and Rationale 
In numerous recent studies, deep neuronal networks (DNN) have been leveraged to examine the 
usefulness of artificial intelligence (AI)-based DNN for diagnostic purposes (summarized by 
Topol et al. 2019). In essence, they have successfully proved to recapitulate state-of-the-art 
diagnoses currently performed by humans.  
Specifically, the use of artificial intelligence for pattern recognition showed that DNN could 
categorize complex and composite data points, chiefly images, with high fidelity to a specific 
pathogenic condition or disease. The majority of these studies are primarily based on extensive 
training sample collections that were categorized a priori. Subsequently, this "training" provided 
the necessary input to classify newly delivered specimens into the correct subgroups, frequently 
even outperforming independent human investigators. So far, these studies have thus provided the 
rationale for the use of DNN in real-world diagnostics. However, the prerequisite for using DNN 
in a real-world setting, where specimen sampling and analysis would need to outperform human 
diagnosis prospectively, would be a blinded and prospective trial. Currently, there is a lack of 
prospective data, therefore still challenging the notion that DNN can outperform state-of-the-art 
human-based diagnostic algorithms. Here we want to investigate the validity and usefulness of AI-
based diagnostic capabilities prospectively in a real-world setting. 
Hematologic diagnostics heavily rely on multiple methodically distinct approaches, of which 
phenotyping aberrant blood or bone marrow cells from affected patients represents a cornerstone 
for all subsequent methods, such as chromosomal or molecular genetic analyses. At the MLL, five 
different diagnostic pillars are required to provide diagnostic evidence for a specific malignant 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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blood disorder faithfully: cytomorphology and immunophenotyping first, guiding more specific 
methods such as cytogenetics, FISH, and a diversity of molecular genetic assays. 
Objectives 
Phenotyping of blood cells is primarily based on two distinct challenges; (1) the morphological 
appearance and abundance of specific cell types and (2) the presence of particular lineage markers 
detected by flow cytometry. These two methods are critical for each subsequent decision-making 
process and, thus ultimately, the final diagnosis. Simultaneously, these two methods are ideally 
suited for automated analysis by DNN due to their inherent image-based nature. This has been 
recently illustrated by a publication by Marr and colleagues (Matek et al., 2019) 
In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral 
blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a benefit 
for diagnostic quality and, ultimately, patient care. Moreover, BELUGA will provide evidence for 
the cooperative nature of image-based diagnostic tools for other pillars of hematologic diagnostic 
decision making such as genetic and molecular genetic characterization. 
BELUGA, therefore, consists of three parts (Fig. A-C). In A, we want to train a DNN with an 
unprecedented collection of blood smears and flow-cytometry-based data points collected during 
the course of 15 years. These samples consist of all hematological malignancies currently 
identified and recognized by the current WHO classification for hematologic malignancies. Due 
to the varying incidences of these entities, the total number of training items varies from 1,000 to 
20,000 for 15 years. However, we deem this discrepancy a benefit to this trial's overall aims, 
because this diverse spectrum will inform us on the number of training items needed for 
outperforming the state-of-the-art diagnostics in cytomorphology or flow cytometry. 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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In part B, we will compare the overall performance of our trained DNN prospectively to new yet 
undiagnosed samples arriving at our laboratory (see the main section for details). The superiority 
of DNN based categorization will be challenged based on the pre-defined outcome parameters 
accuracy with respect to state-of-the-art diagnostics, mismatch-rate, and time needed to provide a 
diagnostic probability. 
Lastly, in C, we will investigate the effects on faster and more accurate diagnostic power by 
leveraging our trained DNN to aid downstream diagnostic methodologies such as chromosomal 
analysis or panel sequencing of patient samples. 
Graphical Abstract BELUGA. A. Training set using retrospective data. B. A prospective trial investigating 
phenotyping analysis by state-of-art diagnostics versus DNN based clustering C. Downstream effect of DNN based 
clustering on downstream diagnostic methods, chiefly chromosomal analysis, molecular genetics, and panel 
sequencing. 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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STUDY DESIGN 
Type of Study 
BELUGA is conducted as a monocentric prospective nested case-control study. The study 
population consists of all patients with suspected hematological disorders for which samples are 
provided to the MLL between 01.08.2020 and 31.07.2021. BELUGA is a non-interventional study 
without therapeutic consequences for direct patient care. 
Our technology will also be of particular interest for studies of pathophysiological precursors of 
disease, since we will be able to obtain discrete and continuous readouts. This will enable us to 
scrutinize a priori diagnosis with internal diagnosis and, at the same time, compare the most 
probable diagnostic decision with the actual patient disease course over time.  
 
 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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Duration of Study 
Retrospective analysis reveals that the MLL currently provides a state-of-the-art diagnosis for 60% 
of all hematological disorders detected in the Federal Republic of Germany. Specifically, the MLL 
provides a therapy-consequent diagnosis for all current hematological malignancies recognized by 
the WHO classification (Swerdlow et al. 2017). Thus, the number of samples according to their 
respective entity reflects incidence in the general population. Diagnosis is provided to external 
caregivers in a seven-day workflow, with new samples arriving at the MLL facility every day. 
The mean number of daily samples processed amounts to 280. From these, 50% are primary 
diagnoses and 50% follow-up assessments of disease, most importantly MRD diagnosis in CML. 
BELUGA will be conducted over the entire course of 52 weeks; therefore, each entity statistically 
will be present with at least 50 novel diagnoses.  
Quality Control 
Due to the heterogeneity in the quality of blood samples arriving at the MLL, BELUGA is 
preceded by a rigorous quality assessment of every patient sample potentially included in the 
present study. We deem this measure essential to exclude as many bias sources as possible. 
Subsequently, to prevent any shortcoming of diagnosis to direct patient care, every sample will 
receive the routine diagnostic work-up routinely performed at our institution. 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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Primary Study Endpoints 
Endpoint assessment 
Due to this trial's exploratory nature, statistical testing will be performed at the one-sided 5% level. 
However, if the direction of the hypothesis is not entirely self-evident, or two-sided, two-sided 
tests will be performed. 
Subsequently, a noninferiority test will be performed at the one-side 5% level to test whether AI 
aided is non-inferior to gold-standard testing. This will be instrumental in understanding the value 
of AI guided diagnosis in a real-world setting. 
Quantitative variables will be described with the number of non-missing values, mean, standard 
deviation (SD), median, and minimum/maximum values. Qualitative variables will be defined with 
the number and percentage of patients with each qualitative characteristic. Missing values will not 
be included in the calculation of percentages. 
The efficacy data will be descriptively summarized and used for exploratory purposes only. 
Primary endpoint 
As a primary endpoint, we will examine the ability of DNN to classify disorders according to (after 
initial assessment disease/healthy) to the gold-standard diagnosis. The gold-standard diagnosis is 
defined as an integrated diagnosis, including cytomorphology, flow cytometry, cytogenetics, 
FISH, and molecular genetics. DNN will independently provide a bi-directional (probabilistic) 
diagnosis, with the most probable diagnosis. The primary analysis will include a direct comparison 
between the human cytomorphological examination and the pattern recognition software. 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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Secondly, this result will be provided to downstream diagnostic departments to assess phenotypic 
diagnosis's usefulness for genetic characterization. We hypothesize that the turn-around time will 
be significantly enhanced, further providing quality at sooner timepoint. 
Secondary Endpoints 
As secondary endpoints, we will examine disease-specific measurements with respect to clinical 
decision making based on either the AI guided diagnosis or the internal gold standard. Due to the 
holistic nature of DNN in assessing the overall "status-quo" of the disease, we envisage a 
stratification change with disease subtypes. Moreover, disease-specific items such as blast count 
in acute myeloid leukemia will be compared between automated DNN-based diagnosis and 
classical cytomorphology. 
 
 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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SUBJECT SELECTION AND WITHDRAWAL 
Number of Subjects 
The training cohort of BELUGA consists of 50,000 annotated samples for which 
cytomorphological smears (25,000 samples) and immunophenotyping (25,000 samples) data 
points have been collected. This cohort serves as a foundation for the DNN to perform training. 
Our test cohort will consist of all samples for which cytomorphology and immunophenotyping 
will be performed for one year. Due to BELUGA's holistic nature, we want to ensure sufficient 
statistical power for each distinct entity throughout the study. 
Gender and Age 
Patients samples from both sexes will be used (male and female). Only samples from adult patients 
(i.e. 18 years or older) will be used. 
Inclusion Criteria 
§ Patients having been diagnosed with a suspected hematological disorder: 
§ The suspected diagnoses constitute a primary diagnosis 
§ Only samples of patients min. 18 years of age will be used 
§ Samples must suffice quality attributes control which are denoted in “Exclusion Criteria” 
Exclusion Criteria 
§ The sample is not fit for state-of-the-art diagnosis, fails initial quality control. For quality 
insurance, we will exclude samples in heparin- instead of EDTA. Samples with damage 
due to atmospheric reasons (freeze-thaw damage or elevated temperature) will be excluded. 
§ Samples with too scarce material jeopardizing routine gold-standard diagnosis will be 
excluded. 

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§ Bone marrow aspirates without sufficient material to assess malignant or healthy 
hematopoiesis.  
Subject Identification & Recruitment 
A unique MLL_identifier routinely identifies patients. This ID is not traceable to the patient's 
identity, and thus full anonymization/pseudonymization is ensured. 
Location 
BELUGA will be conducted as a monocentric trial at the MLL. 
 
 

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STATISTICAL PLAN 
Sample size 
A sample size of N approximately 25,000 samples per method is expected to achieve sufficient 
power for statistical significance testing; preliminary data suggest that machine learning-based 
classification achieves an accuracy of 95 – 98% compared to conventional diagnostics. This leads 
us to estimate the power of 1.00 for an a=0.05 for testing conventional and DNN categorization.  
Statistical Methods 
Quantitative variables will be described with the number of non-missing values, mean, standard 
deviation, median, and minimum/maximum values. Qualitative variables will be expressed as a 
number and percentage of patients with each qualitative characteristic. The missing values are not 
intended to be included in the calculation of percentages. Sensitivity and specificity will be 
assessed specific to each method, with respect to internal gold standard diagnostic work-up at two 
different time points, first in comparison to our initial phenotypic diagnosis and lastly, in 
comparison to our final assessment based on the genetic and chromosomal analysis. 
 
RISKS AND BENEFITS 
Potential Direct Benefits to Subject 
Conducting BELUGA will not bear any risks for patients enrolled in the study. Routine state-of-
the-art diagnosis is provided for each sample, and prioritization of sample material in favor of 
current diagnostic material and reporting is performed to prevent jeopardization of gold standard 
diagnosis. 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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DATA HANDLING AND RECORD 
Data management documents will be generated under the responsibility of the sponsor. A 
management plan will be issued before data collection begins and will describe all functions, 
processes, and specifications for data collection, cleaning, and validation. 
The data management documents will describe analysis methods and individuals who are 
authorized to enter the data, decisions about ownership of data, source data storage, the origin and 
destination of the data, and who will get access to the data at all times. 
Data Management Responsibilities are primarily handled by co-investigator Wolfgang Kern (WK) 
Upon request of external researchers, the sponsor will provide these investigators with additional 
data relating to the trial, duly anonymized and protected according to applicable requirements. 
STUDY MONITORING, AUDITING, AND INSPECTION 
The Investigator will make all the trial-related source data and records available at any time to 
quality assurance auditor(s) mandated by the sponsor, or to domestic/foreign regulatory inspectors 
or representatives who may audit/inspect the trial. 
The primary purposes of an audit or inspection are to assess compliance with the trial protocol and 
the principles of ICH-GCP, including the Declaration of Helsinki and all other relevant regulations. 
FINANCIAL CONSIDERATIONS 
The MLL is the sole sponsor of this trial. 
 
 

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in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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CONFLICT OF INTEREST  
Prof. Dr. med. Dr. phil. Torsten Haferlach and Prof. Dr. med. Wolfgang Kern are part owners of 
the Munich Leukemia laboratory. Adam Wahida receives a research fellowship from the Torsten-
Haferlach Leukemia Diagnostics Foundation. 
PUBLICATION PLAN  
Results from BELUGA will be generated and analyzed according to the guidelines of the Good 
Scientific Practice of the German Science funding agency (DFG). Results that will be interesting 
for the scientific community will be submitted and subsequently published in peer-review based 
journals, according to the appropriate scope and audience. 
At the end of the trial, one or more manuscripts for joint publication may be prepared in 
collaboration between the Investigator(s) offered authorship and the sponsor. The sponsor reserves 
the right to be the last author(s) in all publications related to this trial. In the event of any 
disagreement in the content of any publication, both the Investigator's and the sponsor's opinion 
will be fairly and sufficiently represented in the publication. 
 
 

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ARCHIVING 
The PI is responsible for maintaining all the records (protocol and protocol amendments, relevant 
correspondence, and all other supporting documentation), which enable the conduct of the trial at 
the site to be fully understood, in compliance with ICH-GCP.  
The study site should plan on retaining such documents for ten years after study completion. These 
documents should be retained for a more extended period if required by the applicable regulatory 
requirements or the hospital, institution, or private practice in which the study is being conducted. 
Patient identification codes (patient names and corresponding study numbers) will be retained for 
this same period.  
Trial Master File 
The sponsor will archive the Trial Master File in accordance with ICH-GCP and applicable 
regulatory requirements. 
 
 
 

The information provided in this document is confidential and proprietary to the MLL Munich Leukemia Laboratory. The information 
in this document shall not be disclosed to any third party, in any form, without the prior written consent of the MLL 
 
 
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REFERENCES 
 
- 
Investigators of this study are denoted in Bold 
 
Haferlach T. 2019. Taschenatlas Hämatologie: Mikroskopische und klinische Diagnostik für die 
Praxis. Georg Thieme Verlag. 
Haferlach T. 2020. Hämatologische Erkrankungen. Springer https://doi.org/10.1007/978-3-662-
59547-3. 
Krappe S, Benz M, Wittenberg T, Haferlach T, and Münzenmayer C. 2015. “Automated 
Classification of Bone Marrow Cells in Microscopic Images for Diagnosis of Leukemia: A 
Comparison of Two Classification Schemes with Respect to the Segmentation Quality.” Medical 
Imaging 2015: Computer-Aided Diagnosis. https://doi.org/10.1117/12.2081946. 
Krappe S, Wittenberg T, Haferlach T, and Münzenmayer C. 2016. “Automated Morphological 
Analysis of Bone Marrow Cells in Microscopic Images for Diagnosis of Leukemia: Nucleus-
Plasma Separation and Cell Classification Using a Hierarchical Tree Model of Hematopoesis.” 
Medical Imaging 2016: Computer-Aided Diagnosis. https://doi.org/10.1117/12.2216037. 
Matek, C, Schwarz S, Spiekermann K, and Marr C. 2019 “Human-Level Recognition of Blast 
Cells in Acute Myeloid Leukemia with Convolutional Neural Networks.” Nature Machine 
Intelligence. https://doi.org/10.1101/564039. 
Swerdlow, S. H., Elias Campo, N. Lee Harris, E. S. Jaffe, S. A. Pileri, H. Stein, J. Thiele, et al. 
2017. “WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues (Revised 4th 
Edition).” IARC: Lyon 421. 
Zhao M, Mallesh N, Höllein A, Schabath R, Haferlach C, Haferlach T, Elsner F, Lüling H, 
Krawitz P, and Kern W.  “Hematologist‐Level Classification of Mature B‐Cell Neoplasm Using 
Deep Learning on Multiparameter Flow Cytometry Data.” Cytometry, June 2020. 
https://doi.org/10.1002/cyto.a.24159. 
 
 
Matek, C, Krappe S, Münzenmayer C, Haferlach T, Marr C “Highly accurate differentiation of 
physiological and pathological bone marrow cell morphologies using deep residual networks” 
paper sent out for formal peer review 5/2020