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\begin{document}

\title{Deep learning analysis of mid-infrared microscopic imaging data for the
diagnosis and classification of human lymphomas}



\author[1]{Philipp Zelger}%
\author[1]{Andrea Brunner}%
\author[1]{Bettina Zelger}%
\author[2]{Ella Willenbacher}%
\author[3]{Seraphin Unterberger}%
\author[3]{Roland Stalder}%
\author[4]{Christian Huck}%
\author[2]{Wolfgang Willenbacher}%
\author[1]{Johannes Pallua}%
\affil[1]{Medical University of Innsbruck}%
\affil[2]{Medizinische Universität Innsbruck}%
\affil[3]{Leopold Franzens Universität für Innsbruck}%
\affil[4]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
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\selectlanguage{english}
\begin{abstract}
The present study presents an alternative analytical workflow that
combines mid-infrared (MIR) microscopic imaging and deep learning to
diagnose human lymphoma and differentiate between small and large cell
lymphoma. We could show that using a deep learning approach to analyze
MIR hyperspectral data obtained from benign and malignant lymph node
pathology results in high accuracy for correct classification, learning
the distinct region of 3900 cm-1 to 850 cm-1. The accuracy is above 95\%
for every pair of malignant lymphoid tissue and still above 90\% for the
distinction between benign and malignant lymphoid tissue for binary
classification. These results demonstrate that a preliminary diagnosis
and subtyping of human lymphoma could be streamlined by applying a deep
learning approach to analyze MIR spectroscopic data.%
\end{abstract}%



\sloppy


\textbf{Deep learning analysis of mid-infrared microscopic imaging data
for the diagnosis and classification of human lymphomas}

\emph{Short title: Deep learning analysis of Mid-infrared microscopic
human lymphomas imaging data}

\emph{P. Zelger\textsuperscript{1*}, A. Brunner\textsuperscript{2*}} ,
B. Zelger\textsuperscript{2}, E. Willenbacher\textsuperscript{3}, S. H.
Unterberger\textsuperscript{4}, R. Stalder\textsuperscript{5}, C. W.
Huck\textsuperscript{6}, W. Willenbacher\textsuperscript{3,7}, J. D.
Pallua\textsuperscript{8\#}

\emph{\textsuperscript{1}University Hospital of Hearing, Voice and
Speech Disorders, Medical University of Innsbruck, Anichstrasse 35, 6020
Innsbruck, Austria}

\selectlanguage{ngerman}\emph{\textsuperscript{2}Institute of Pathology, Neuropathology and
Molecular Pathology, Medical University of Innsbruck, Müllerstraße 44,
6020 Innsbruck, Austria}

\emph{\textsuperscript{3}University Hospital of Internal Medicine V,
Hematology \& Oncology, Medical University of Innsbruck, Anichstrasse
35,6020 Innsbruck, Austria}

\emph{\textsuperscript{4}Institute of Material-Technology,
Leopold-Franzens University Innsbruck, Technikerstraße 13, 6020
Innsbruck, Austria}

\emph{\textsuperscript{5}Institute of Mineralogy and Petrography,
Leopold-Franzens University Innsbruck, Innrain 52 6020 Innsbruck,
Austria}

\emph{\textsuperscript{6}Institute of Analytical Chemistry and
Radiochemistry, Innrain 80-82, 6020 Innsbruck, Austria}

\emph{\textsuperscript{7}Oncotyrol, Centre for Personalized Cancer
Medicine, Karl-Kapferer-Straße 5, 6020 Innsbruck, Austria}

\emph{\textsuperscript{8}University Hospital for Orthopaedics and
Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020
Innsbruck, Austria}

*both authors contributed equally

\textsuperscript{\textbf{\#}}\textbf{Corresponding author:}

Priv.-Doz. MMag. Dr. Johannes Dominikus Pallua MSc PhD

University Hospital for Orthopedics and Traumatology,

Medical University of Innsbruck,

Anichstraße 35, 6020 Innsbruck, Austria

\emph{Mail: Johannes.Pallua@i-med.ac.at}



\textbf{Abstract}

The present study presents an alternative analytical workflow that
combines mid-infrared (MIR) microscopic imaging and deep learning to
diagnose human lymphoma and differentiate between small and large cell
lymphoma. We could show that using a deep learning approach to analyze
MIR hyperspectral data obtained from benign and malignant lymph node
pathology results in high accuracy for correct classification, learning
the distinct region of 3900 cm\textsuperscript{-1} to 850
cm\textsuperscript{-1}. The accuracy is above 95\% for every pair of
malignant lymphoid tissue and still above 90\% for the distinction
between benign and malignant lymphoid tissue for binary classification.
These results demonstrate that a preliminary diagnosis and subtyping of
human lymphoma could be streamlined by applying a deep learning approach
to analyze MIR spectroscopic data.

\textbf{Keywords:} deep learning, mid-infrared microscopic imaging,
diffuse large B-cell-lymphoma, follicular lymphoma

\textbf{Abbreviation:}

\textbf{ANN:} neural network

\textbf{CAE:} Convolutional Autoencoder

\textbf{DA:} discriminant analysis

\textbf{DLBCL:} diffuse large B-cell lymphoma

\textbf{FC:} follicle centre

\textbf{FL:} follicular lymphoma

\textbf{GCB:} germinal centre

\textbf{HCA:} hierarchical cluster analysis

\textbf{MIR:} Mid-infrared

\textbf{MZ:} mantle zone

\textbf{non-GCB:} non-germinal centre

\textbf{PCA:} principal component analysis

\textbf{PLS:} partial least squares

\textbf{RF:} random forest

\textbf{rLN:} Reactive (normal) lymph nodes

\textbf{SVMs:} support vector machines

\textbf{Introduction}

Mid-infrared (MIR) microscopic imaging is a modern analytical method
that is widely used to characterize the components of biological
specimens {[}1-3{]} and has already been applied in tissue histology
{[}4-6{]}. MIR microscopic imaging was, for example, used to aid in the
differentiation between benign and malignant disease {[}3, 5, 7-11{]}
and was tested for imaging lymph node histopathology {[}6, 12{]}. Our
group successfully implemented this method for the differentiation
between reactive lymph nodes, small and large cell lymphoma using
follicular lymphoma (FL), and diffuse large B-cell lymphoma (DLBCL) as
an example {[}13{]}.

IR microscopic imaging experiments are measurements that include
high-quality and high-quantity information. Thus, chemometric tools in
imaging analysis are a prerequisite to taking advantage of the entire
measurement {[}14{]}. Statistical classification methods have been used
for histopathological studies, which discriminate between pixels of
healthy tissue versus pixels of diseased tissue {[}15, 16{]}.
Multifactorial statistical analysis methods related to IR data have been
widely implemented for identifying changes in lipids, proteins, nucleic
acids, and carbohydrates, such as principal component analysis (PCA)
{[}17, 18{]} and partial least squares (PLS) {[}19, 20{]} combined with
discriminant analysis (DA) {[}21{]}, hierarchical cluster analysis (HCA)
{[}22{]}, support vector machines (SVMs) {[}23{]} and random forest (RF)
{[}24{]}. As a pattern-recognition-based approach, the (artificial)
neural network ((A)NN) proved to be effective in analyzing data obtained
from biological specimens, also using IR imaging techniques {[}25-27{]}.
NNs perform best when trained with a large amount of labelled data
{[}28, 29{]}. However, in pathology, labelling data, which means
(whole-slide) images, is complex and time-consuming {[}28{]}. These
limitations can be eliminated by employing so-called self-supervised and
unsupervised techniques {[}28, 29{]}. One unsupervised technique is the
Convolutional Autoencoder (CAE), which is trained with unlabelled data
{[}29, 30{]}. Such NNs were developed to work with high-dimensional data
and have already been implemented to solve medical problems, such as
radiology, cardiology, neurology, and even pathology {[}30-34{]}. In
brief, the input layer of the CAE compresses the data and creates a
code, which is then used to reconstruct them in the output layer
{[}30{]}. The CAE-based network consists of several convolutional layers
with a decreasing number of filters that create a bottleneck in the
centre of the network (\textbf{Figure 1a} ). After this bottleneck, the
number of neurons increases again, allowing the network to reproduce the
input data. The bottleneck in the centre of the CAE forces the NN to
learn the features of the input data. This can be compared to a
dimensionality reduction as performed with PCA {[}30{]}.

This study combines MIR imaging of unstained tissue slides and a deep
learning approach using CAE as an example of an unsupervised technique
to differentiate between benign and malignant lymphoid tissues and to
classify lymphoma subtypes.

\textbf{Materials and Methods}

Patients and Material

The dataset acquired as part of our previous study on MIR imaging of
human lymphomas was used {[}13{]}. This dataset includes MIR
measurements of six cases of FL and 12 cases of DLBCL (five germinal
centre (GCB) and seven non-germinal centre (non-GCB) subtypes), which
were diagnosed between 2002 and 2012 at the Institute of Pathology,
Neuropathology, and Molecular Pathology, Medical University of
Innsbruck. Reactive (normal) lymph nodes (rLN) served as control. The
study was conducted according to the IHC-GCP guidelines and the
declaration of Helsinki. Ethical approval was obtained from the ethical
committee of the Medical University of Innsbruck (EK-Nr.1213/2017). For
information on initial sample preparation and MIR imaging, please refer
to Willenbacher E et al. (2021) {[}13{]}.

Data Processing

In order to classify data by a neural network, the network has to learn
a feature representation of the data. Usually, this is achieved by
training on a big set of labelled data. Labelling histological data,
however, is time-consuming and expensive. A solution to that issue is to
train the network in an unsupervised manner. To do so we implemented a
convolutional autoencoder (CAE). Those neural networks have a bottleneck
in their architecture. This bottleneck forces the network to learn a
reduced representation of the data. This reduced representation can then
be used as input for a classifier network.

We fed the single-pixel spectra of the two-dimensional dataset into the
ANN. A total number of over 1 million single spectra from different IR
datasets was used to pre-train the CAE. Using a large variety of data
allows the neural network to learn the features of the IR data without
the danger of overfitting the network {[}29{]}.

The neural network was implemented using Python and Tensorflow. The
training of the Autoencoder took around 4 hours on an NVidia GTX 1080
GPU. After this network preparation, the so-called decoder part (blue
layers in \textbf{Figure 1a} ) of the Autoencoder has been removed. The
updated neural network ends at the bottleneck. All the layers are frozen
at this point, which means they cannot be altered anymore by further
training. With that, the previous feature extraction capabilities are
preserved. Next, a small neural network consisting of two layers with
ten fully connected neurons is added to the network. These two layers
are connected to the output of the network, which, depending on the
task, consists of several (for multiclass classification) or one (for
binary classification) output neurons (\textbf{Figure 1b} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} \textbf{a) S} chematic of the CAE. The CAE receives
an IR spectrum as input and tries to reproduce this input data. The pink
convolutional layers have a Kernel size of 10 with 32,64, and 32
filters. An additional Dropout layer (20\% dropout rate) was added to
prevent overfitting. The green layer describes a fully connected dense
layer with 24 neurons. The blue part of the network consists of three
upscaling convolutional layers with a kernel size of 3,5 and 4,
respectively, and 64,128 and 55 Filters. The total number of parameters
accounts for 800.863 parameters. \textbf{b)} The green layers represent
the two added dense layers with 10 neurons for the classification tasks.
The grey squares represent output neurons. The number of output neurons
depends on the number of classes to be distinguished. The trainable
parameters account for 261.

This final part of the network will be trained to classify the different
subtypes of lymphoma and normal (reactive) control. It uses the
pre-trained feature detection of the first part of the Autoencoder. With
that, the number of parameters to be fitted is reduced to 253
Parameters.

Labelling training and test data are required to train the classifier
part of the neural network. Here, areas of interest in lymphoma tissue
(FL = follicular and intrafollicular area; DLBCL, non-GCB and DLBCL, GCB
subtype) and rLN were labelled, and the corresponding spectra were
extracted. One sample served as a training set, and the other as an
evaluation set. The labelling procedure of the training data is depicted
in \textbf{Figure 2} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2} Workflow for the labelling and extraction of the
training and evaluation spectra of the different classes. The first row
shows the optical images of the FL (a), DLBCL (b and c), and normal
control (e). The second row shows the labelled regions. The coloured
labels correspond to FL (grey), DLBCL, non-GCB subtype (orange), DLBCL,
GCB subtype (pink), and rLNs (green = follicle centre (FC); blue =
mantle zone (MZ)). The right column shows a selection of spectra, with
the type corresponding to the coloured bar on the left.

\textbf{Table 1} shows the type of disease, label colour as manually
assigned (see figure 2), and the number of spectra obtained for
analysis.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Type} & \textbf{FL} & \textbf{DLBCL, non-GCB} & \textbf{DLBCL,
GCB} & \textbf{rLN\textsuperscript{FC}} &
\textbf{rLN\textsuperscript{MZ}}\tabularnewline
\midrule
\endhead
\textbf{Colour in Fig. 2} & Grey & Orange & Pink & Green &
Blue\tabularnewline
\textbf{Nr. Spectra} & 45.000 & 40.000 & 50.000 & 35.000 &
45.000\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1.} Type of tissue, the colour of label and number of
spectra obtained for this study

After labelling, the following steps were carried out:

Application of the network to distinguish between benign and malignant
lymphoid tissue types. To do so, the neural network is trained with two
sets of spectral data (e.g., FL vs DLBCL, non-GCB). The evaluation of
the deep classifier is done on the second set of spectra containing the
respective classes.

Classification of more than two types (multiclass) of neoplastic and
reactive lymphoid tissue.

\textbf{Results}

\textbf{Figure 3} shows the binary (left side) and multiclass (right
side) classification obtained from MIR imaging data applying the deep
learning approach described above.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} Confusion matrices for the binary (right side) and
multiclass (left side) classification between the benign and malignant
lymphoid tissues. The x-axis shows the true label. The y-axis
corresponds to the predicted label.

As shown in \textbf{Figure 3,} the accuracy is above 95\% for every pair
of malignant lymphoid tissue and still above 90\% for the distinction
between benign and malignant lymphoid tissue. The multiclass
classification between the lymphoma types (FL and DLBCL) and
rLN\textsuperscript{FC} and rLN\textsuperscript{MZ} leads to similarly
high accuracy for the binary classification. This multiclass
classification's accuracy is still above 94 \% for all neoplastic and
reactive lymphoid tissue types.

The neural network, once trained, performs a forward execution of a
complex function depicted by the NN, representing the analysis of an IR
image in less than 25 ms. NNs with three output classes have been
trained to visualize the capabilities of the deep learning approach.
These three classes serve as a colour code for an RGB image, whereby the
NN classifies the spectrum of each pixel. This means that it was decided
to which class the corresponding subsection belongs for each pixel
individually. This leads to the satisfactory resolution images shown
in\textbf{Figure 4 A-C} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4 a)} Results of the classification of the NN for FL
showing histology on the left side and the right side neural network
classification with FL (follicular/intrafollicular) in red, GCB in green
and normal tissue (rLN\textsuperscript{FC}) in blue.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 4 b)} Results of the classification of the NN for DLBCL,
non-GCB showing histology on the left side and on the right side neural
network classification with DLBCL, non-GCB in blue, GCB in green and
normal tissue (rLN\textsuperscript{FC} ) in red.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 4 c)} Results of the classification of the NN for
rLN\textsuperscript{FC/MZ} showing histology on the left side and the
right side neural network classification with rLN\textsuperscript{FC} in
green and rLN\textsuperscript{MZ} in red and FL in blue.



\textbf{Discussion}

Our study showed that using a deep learning approach to analyze MIR
imaging data from unstained histological slides can distinguish between
benign and malignant lymphoid tissue and aid in classifying types of
lymphomas. MIR imaging on tissue sections results in large and complex
data sets that must be analyzed and interpreted. Focussing on possible
use in routine diagnostics, the data acquisition and the subsequent
analysis must take place within a narrow time window to offer an
advantage over standard pathological diagnostics or assist the
pathologist during the diagnostic procedure. A deep learning approach
using NNs, offers such a possibility. Our NN, once trained, analyzed a
MIR image in less than 25 ms. However, training NNs using a deep
learning approach takes considerable time and requires graphical
processing units (GPUs) and high-performance machines to process digital
images {[}28, 30{]}. The training, however has to be performed only
once, and the NN can then be used to classify IR data without further
training. This even allows for real-time spectrum analysis.

NNs have already been implemented in medicine and used in various
studies in the fields of radiology, cardiology, neurology, and pathology
{[}30-34{]}. But several caveats prevent such techniques from being
widely used. One of the most important is undoubtedly the ''black box''
character of such analyses {[}29, 30{]}. The decisions of such
algorithms are not easy for the human user to understand and interpret.
Therefore, a certain scepticism about such approaches is understandable,
especially in medicine, where comprehensible decisions with sometimes
severe consequences for patients must be made and justified {[}28,
29{]}.

Another problem is the need for large datasets to train NNs for a
specific problem {[}28, 30{]}. Small datasets result in poor performance
of NNs, while on the other hand, one has to bear in mind not to
''overfit'' a NN {[}29, 30{]}. In the case of pathological diagnoses,
obtaining such datasets is a significant problem, especially regarding
rare diseases. But one has also to consider the extreme variability of
histopathological patterns derived from various tissue types, building
up organs {[}28{]}. Additionally, data quality is essential, especially
when dealing with pixel-based data (images) in pathology, where
artefacts might pose serious problems {[}28, 35, 36{]}. In fact,
concerning MIR imaging, there are currently no available datasets that
could be used, and it is also questionable if there will be any in the
future. Overall, it is doubtful that any deep learning approach using
NNs, combined with MIR microscopic imaging, will replace pathologists in
the foreseeable future. It is more likely, that deep learning-driven
approaches will be used to assist a pathologist during the diagnostic
procedure {[}37{]}.

Finally, besides technical questions and dataset availability, ethical
and legal questions are also associated with the use of deep learning in
decision-making processes in pathology. These fundamental questions
range from concern about data privacy to the question of responsibility
for a wrong decision based on a deep learning approach {[}29{]}.
However, when there are fewer and fewer pathologists, deep learning
techniques may assist as a diagnostic tool to support the pathologist in
stratifying patients, identifying urgent cases, and thus better
directing the routine workflow.

Finally, the financial side of introducing such technologies must also
be considered. The financial pressure on pathology laboratories is
already a challenge because of the increasing digitization and
subsequent data storage {[}28{]}. The acquisition of access to the
appropriate hard- and software, such as GPU clusters, as a must to train
deep learning algorithms in practice, could fail due to a lack of
funding {[}28, 38{]}.

Our results demonstrate that a diagnosis and subtyping of human lymphoma
could be streamlined based on MIR microscopy and a deep learning
approach for data analysis. This might be a complementary pathway for a
quick preliminary assessment of the type and aggressiveness of the
disease and could probably help in advance to identify urgent diagnoses
and, given the increasing shortage of pathologists, to prioritize these
patients in the routine workflow.

\textbf{Acknowledgements}

The authors would like to thank Sabine Joebstl for her excellent
technical assistance.

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Comput 2014 (2014) 89-96.\selectlanguage{english}
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