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OPEN

Training high‑performance deeplearning classifier for diagnosis
in oral cytology using diverse
annotations
Shintaro Sukegawa 1,2*, Futa Tanaka 3, Keisuke Nakano 2, Takeshi Hara 3,4, Takanaga Ochiai 5,
Katsumitsu Shimada 6, Yuta Inoue 3, Yoshihiro Taki 3, Fumi Nakai 1, Yasuhiro Nakai 1,
Takanori Ishihama 1, Ryo Miyazaki 7, Satoshi Murakami 6, Hitoshi Nagatsuka 2 &Minoru Miyake 1
The uncertainty of true labels in medical images hinders diagnosis owing to the variability acrossprofessionals when applying deep learning models. We used deep learning to obtain an optimal
convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology
considering labels from multiple oral pathologists. Six whole-slide images were processed using
QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting
in 14,535 images with the corresponding pathologists’ annotations. Data from three pathologists who
provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigatedsix models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4)GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide
dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statisticalevaluation was performed repeatedly and independently using every slide 10 times as test data. For
the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the
probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967).
For the models using the pathologists and GT annotations, many slides showed very low accuracy
and large variations across tests. Hence, the classifier trained with probabilistic labels provided the
optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These
results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses ofvarious professionals.

Keywords Deep learning, Oral cytology, Classification, Convolutional neural network, Probabilistic labeling
Early detection and treatment are important strategies for oral ­cancer1. Remarkably, oral exfoliative cytology
is extremely easy and minimally invasive because it allows testing by swabbing. It is also useful as a primary
screening tool to identify unrecognized diseases in individuals without signs or symptoms as well as for simple
differential diagnosis of oral dysplasia and malignant tumors before histological e­ xamination2. Cytology is mainly
performed by direct smearing on a glass slide. In recent years, liquid-based cytology has become popular. In
this method, the collection device is placed into a vial containing a fixative preservation solution immediately
after cell collection to prevent cell drying. The cell specimen is prepared using a specialized device to perform

1

Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750‑1, Ikenobe,
Miki‑Cho, Kita‑Gun, Kagawa 761‑0793, Japan. 2Department of Oral Pathology and Medicine, Graduate School of
Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700‑8558, Japan. 3Department
of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1‑1 Yanagido,
Gifu, Gifu 501‑1193, Japan. 4Center for Research, Education, and Development for Healthcare Life Design,
Tokai National Higher Education and Research System, 1‑1 Yanagido, Gifu, Gifu 501‑1193, Japan. 5Division of
Oral Pathogenesis and Disease Control, Department of Oral Pathology, Asahi University School of Dentistry,
Mizuho 501‑0296, Japan. 6Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental
University, 1780 Hirooka‑Gobara, Shiojiri, Nagano 399‑0781, Japan. 7Stony Brook Cancer Center, Stony Brook
University, Stony Brook, NY 11794, USA. *email: gouwan19@gmail.com
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a standardized procedure. This improves the diagnostic accuracy of oral cytology by revealing complex cellular­patterns3. In recent years, the introduction of whole slide imaging (WSI) has revolutionized pathological diagnosis. WSI captures glass slides as high-quality digital images for easy access and sharing. Additionally, the ability
to process images has enabled learning and development using artificial i­ ntelligence4.
In clinical and research environments, we previously studied a cytology convolutional neural network (CNN)
for diagnosis based on deep l­earning5. The diagnostic accuracy for the oral cytology patch images using the
CNN reached 0.9016, indicating reliable diagnosis using the CNN. However, the annotations were merged intoa single result after consultation with multiple pathologists. Rather than making judgments based on the entire
histopathological image, a pathologist’s diagnosis often differs depending on segmented patch images. This can beobserved during oral cytology, which involves a complex mixture of cells. To implement deep learning for diagnosis trusted by pathologists, measures for annotating data considering diverse diagnoses by multiple pathologists
should be developed. Annotation diversity is a problem common to image-based medical diagnosis and has
the following characteristics that differ from classification for other types of images. Uncertainty about the truelabel is a problem because different medical professionals often assign different labels to the same ­image6. A classification model for unevenly labeled data remains largely unexplored. Using probabilistic labels may be a viable
alternative to train deep learning classification models for integrating and processing diversely labeled ­data7.
The method of generating accurate answers presented in this study has been explored under the concept ofcollective knowledge in the field of cognitive science. Collective knowledge stems from gathering many opinions
to find a solution to a problem that cannot be solved by one person. While some of these methods are beginningto be utilized in the medical field, applying them to fields such as image diagnosis presents challenges due tovarying opinions among specialists. For example, certain specialists may specifically identify imaging findingsthat elude general physicians. Therefore, AI trained to generate accurate diagnoses reflecting specialists’ uniqueabilities, based on methods selected by their respective specialties, could foster trust among specialists. Whilecollective knowledge has been reported as a method to reflect such expert opinions, they are limited to reports
on lesion detection that are perhaps easily ­detectable8. Moreover, discussions on this method and its integration with neural network results are ­scarce9. Therefore, our study proposes a method centered on comparing
diagnostic opinions among pathologists.

Materials and methods
Study design

This study aimed to use a deep learning model based on a CNN to analyze the classification performance ofdiagnosis using oral exfoliative cytology when labels from multiple pathologists are available and to obtain an
optimized model. This study was based on fully supervised CNN learning.

Ethics statement

The WSI data for oral exfoliative cytology were acquired at Kagawa University Hospital from April 2015 to
October 2022 and used retrospectively. This study was approved by the Ethics Committee of Kagawa University
Faculty of Medicine (registration no. 2022-155). The study had a non-interventional retrospective research design
and was analytical with anonymized data. Thus, the need for informed consent was waived, and no written orverbal informed consent was required from each patient individually. This study was conducted in accordancewith both the ethical guidelines for life science and medical research involving human subjects and the rules
established by the ethics committee.

Image data preparation

In this study, six WSI oral scraping cytology specimens were prepared using the liquid-based method. The
breakdown of the six slides comprised two slides for tongue cancer, two slides for buccal mucosa cancer, one slide
for tongue leukoplakia, and one slide for glossitis. The glass slides were scanned using a Ventana DP 200 slide
scanner (Roche, Basel, Switzerland), and WSI scans were created. Each WSI scan was tiled using the Quantitative
Pathology (QuPath) software (version 0.4.1).
Considering that WSI is compatible with various magnifications, cell pathologies should be evaluated at the
optimal magnification. Therefore, we extracted sections at various magnifications and checked with a pathologist. In QuPath, the magnification can be set using a specific function. We determined a magnification for the
cropped image of 1.0 as the optimal setting for diagnosis by an oral pathologist. The image was a square of 768
pixels per side and was cut into segments overlapping by 192 pixels, obtaining images of 256 × 256 pixels in the
PNG (Portable Network Graphics) format.

Image annotation and selection

Pathologists
The three oral pathologists who performed the annotations were certified by the Japanese Society of Pathology
and had over 15 years of experience.
Image diagnosis
The oral cytology diagnoses from each segmented image were independently labeled. The oral cytology images
were classified into five categories based on the Papanicolaou classification. Classes 1 and 2 were assigned as negative labels, while classes 3–5 were assigned as positive labels. Additionally, each pathologist labeled inconclusive
images that could not be diagnosed owing to factors such as no cells present, out-of-focus imaging, and strongly
overlapping cells. The pathologists were not allowed to consult with each other during annotation.

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Classification of annotations
The labels of each patch image diagnosed by a pathologist were classified. The diagnostic results from the threepathologists are shown in Fig. 1. Based on these results, we defined the three types of labels (Fig. 2):
1. Ground truth (GT) label: Image label assigned when the same diagnosis is provided by the three pathologists.
2. Majority label: Image label assigned when two pathologists provide the same diagnosis, that is, the label withthe highest number of diagnoses is considered correct.
3. Probabilistic label: Image label that encodes the diagnoses of the three pathologists into a single prediction.

Figure 1.  Distribution of data labels.

Figure 2.  Labeling that reflects annotations from multiple pathologists.
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Deep learning model

We selected ResNet50, a representative CNN model, for diagnosis. ResNet is an artificial neural network withhigh accuracy owing to a residual block that adds an input value to the output value of the convolutional layer
to prevent the vanishing gradient problem even when the network is d
­ eep10.
The ResNet50 model was finetuned on the ImageNet dataset. Deep learning classification was implemented
using PyTorch (version 1.13.1 + cu116) and Python (version 3.8.10) (Fig. 3).
The following six deep-learning models were established:
A. Pathologist A model: ResNet50 trained with labels from Pathologist A
B. Pathologist B model: ResNet50 trained with labels from Pathologist B
C. Pathologist C model: ResNet50 trained with labels from Pathologist C
D. GT model: ResNet50 trained with GT labels
E. Majority model: ResNet50 trained with majority labels
F. Probabilistic model: ResNet50 trained with probabilistic labels

Dataset and model training

CNN training was generalized using cross-validation in deep learning. Model validation was performed for allcases, with each slide being the test data for leave-one-out cross-validation to avoid case-by-case overfitting andbias (Fig. 4).
Cross-entropy was used as the loss function, while stochastic gradient descent with momentum was employedfor optimization. This method is an evolved variant of traditional stochastic gradient descent that mitigatesvariability by incorporating moving ­averages11. Additionally, we utilized the cosine annealing learning ratescheduler to ensure stable deep learning. Further details regarding the deep learning parameters are provided
in Supplementary Material S1.
All the models were trained over 300 epochs and had a minibatch size of 32. Training was repeated 10 times
for the deep models using different random seeds.

Figure 3.  A diagram of the deep learning model for each learning label.

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Figure 4.  Diagram of leave-one-out cross-validation adopted in this study.

Performance indicators and statistical analysis

We used the GT labels to construct the test set for all the deep learning models and calculated the accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC) as performanceindicators. Further details on the calculation of these indicators are available in Supplementary Material S2. The
statistical evaluation of the classification performance of each deep learning model was repeated 10 times on
the GT test data. The performance indicators were analyzed using JMP Pro Statistical Software Package Version
16.1.0 (SAS Institute Inc., Cary, NC, USA). We set p < 0.05 to indicate statistical significance. The probabilityof continuous variables was assessed using the Shapiro–Wilk test. The differences in classification performance
between the deep learning models were calculated for all performance indicators using the Wilcoxon test.

Visualization of model diagnosis using activation maps

Visualizing the basis of predictions made by deep learning models is crucial for analysis. Gradient-weighted classactivation mapping (Grad-CAM) enables visualization of relevant image areas per class based on the feature
output from the last convolutional layer of a C­ NN12. We used a heat map and the gradient of the classification
score to identify image areas that contributed to classification.

Results

Comparison of model performance

Supplementary Material S3 shows the performance indicators of AUC and accuracy for each model. Table 1 lists
the statistical test results. In terms of AUC, the probabilistic model showed the highest value in cases 3, 5, and 6.
In terms of accuracy, cases 5 and 6 exhibited the highest values. In contrast, the pathologist A–C and GT models
provided slides with extremely low accuracy and many slides with large variations across trials. The values of
all the performance indicators are listed in Supplementary Material S3. The results for the other performanceindicators were similar to those of the accuracy and AUC, and the probabilistic model consistently showed
high-performance indicators. For cases 5 and 6, the probabilistic model showed a statistically significant highperformance for all indicators.

Visualization of classification using Grad‑CAM

Figure 4 shows a heatmap of the regions of interest for classification using each evaluated deep-learning model.
For ResNet50, Grad-CAM highlighted the feature regions from the final convolutional layer as a heat map.
Positive labeling focused on atypical cells with a high nuclear ratio in the cytoplasm and an increased amountof chromatin in the cell nucleus as a characteristic region. In the top row of Fig. 4, the models of pathologists A
and B focused on the area at the bottom of the image, whereas the model of pathologist C also focused on thearea at the top of the image. Therefore, the majority and probabilistic models focused on both the upper andlower regions of the image for classification. In the bottom row of Fig. 5, each model yielded different predictions.

AUC​

Accuracy

Precision

Recall

F1-score

Probabilistic
vs majority

Probabilistic
vs GT

Probabilistic
vs majority

Probabilistic
vs GT

Probabilistic
vs majority

Probabilistic
vs GT

Probabilistic
vs majority

Probabilistic
vs GT

Probabilistic
vs majority

Probabilistic
vs GT

Case 2

0.007

0.002

0.082

0.039

0.332

0.131

0.056

0.407

0.049

0.157

Case 3< 0.001

0.240

0.079

0.397

0.316

0.468

0.009

0.500

0.311

0.452

Case 4< 0.001< 0.001

0.039

0.359

0.044

0.404

0.030

0.001

0.001

0.082

Case 5< 0.001< 0.001< 0.001< 0.001

0.015

0.027

< 0.001< 0.001< 0.001< 0.001

Case 6< 0.001< 0.001

0.001< 0.001< 0.001< 0.001

0.031

0.006

< 0.001< 0.001

Table 1.  Statistical evaluation results (p-values) comparing probabilistic, majority, and GT models. p < 0.05
indicates a statistically significant difference.

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Figure 5.  Visualization of regions of interest for classification in each evaluated model.
The models of pathologists A–C identified different areas as characteristic regions. Therefore, the prediction andGT labels differed in most models. In contrast, the probabilistic model showed classification predictions thatmatched the GT labels by incorporating each feature region. It is believed that the characteristics of each modelcaused the differences in the classification results.

Discussion

In this study, we evaluated CNNs on datasets with diverse annotations. A deep learning model using probabilistic labels was effective for classification using oral exfoliative cytology annotations provided by multiple oral
pathologists. We then investigated the classification performance for diagnosis using each patch image annotatedby the pathologists. There was a large variation in AUC from 0.799 to 0.692 for pathologist A, from 0.735 to
0.979 for pathologist B, and from 0.803 to 0.972 for pathologist C. For slides that performed well, the performance was close to that of our previous deep learning classifier using patch images (AUC of 0.93)5. However,some slides provided an extremely low accuracy, and large fluctuations occurred across trials. This was because
pathologists generally adhere to a diagnosis (label) and may fail to reach a consensus. Therefore, constructing ahighly generalizable model using a deep learning classification system derived from labels provided by a single
professional posed challenges. Such labeling may be considered inadequate for clinical applications. Additionally,
the significant variation in accuracy among pathologists A–C and GT models suggested that learning based onlabeling from a single person is insufficient. This finding should be considered in pathological diagnosis basedon deep learning.
The simplest method for learning different annotations is majority voting. We applied this method to images
labeled differently by only one of the three pathologists. Most consistent labels showed a relatively stable performance in terms of the AUC, and one case showed a high accuracy. However, the recall often showed poor
performance, possibly influenced by image annotations that were difficult to diagnose. The majority model
may contribute to improving performance on data that show similar feature ­regions13. However, this model has
various disadvantages, especially if the task requires highly specialized knowledge and the majority of labels are
inadequately ­competent14. Therefore, care must be taken when selecting the majority model.
The probabilistic model had no slides with extremely low performance, and three cases showed the highest
AUC among all the models. This model exhibited high robustness without being affected by the test cases. The
probabilistic label considered the diagnoses of all pathologists, thus including all the available data. It has beensuggested that, unlike hard labels, ambiguity may be advantageous for classifying pathological images. However, a liver echo dataset presented an interesting case in the study by Vega et al.6. The performance achievedby a model trained using hard labels showed higher performance than one based only on probabilistic labels.
Compared with the highly reliable case of hard labels, probabilistic labels may converge to incorrect probabilities
and increase prediction errors.
In this study, we investigated learning for pathological image classification using deep learning with labelsthat differed according to the diagnoses of various pathologists. This research attempt has not been reported andholds substantial academic significance. Probabilistic annotations do not rely on the labels of a single pathologist but rather combine the labels of multiple pathologists. Learning involving the differing opinions of multiple
pathologists can establish a reliable method for pathological image classification. In addition, our approach
can be applied to different annotations in other specialized medical fields, such as radiology and echo imaging
diagnosis. In medical imaging, analysis is primarily influenced by the expertise of specialists, leading to different
annotation results. In oral cytology and other imaging diagnoses such as CT and MR, diagnosis is often difficult
even for specialists, and discrepancies in diagnostic test results among specialists are common in clinical practice.
Therefore, we anticipate that our approach to generating accurate answers can extend to AI systems for generalimaging diagnosis. Furthermore, we posit that our system can be applied across various medical situations,
including psychiatric diagnosis and medical interviews. Even in verbal examinations, different physicians may
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have different diagnostic results. Additionally, the utilization of multiple pathology diagnoses during training
can affect the acceptance of deep learning results from pathologists with different opinions or dependency
adjustment cues for the inferred r­ esults13. Therefore, further research on these issues is required. In the future,
we will consider implementing multiple models, such as in ensemble ­learning14 and model ­fusion15, for which
the final prediction combines the outcomes of multiple models, as an additional study on CNNs. In addition, as
an additional study on CNNs, we will consider implementing multiple models, such as in ensemble l­earning16
and model ­fusion17, for which the final prediction combines the outcomes of multiple models to increase theconfidence level.
Our study had various limitations. First, it was impossible to statistically evaluate the performance of eachmodel by combining all data owing to slide-by-slide cross-validation. Obtaining test data from independent slides
is important for evaluating generalizability. Accordingly, we prioritized evaluating the model generalizability inthis study. Second, predictions from three pathologists were considered, but data should be gathered from more
pathologists. However, annotating data requires considerable effort. In future work, more pathologists should
be enrolled to examine and annotate data. Despite the limitations, we believe that our findings will serve as afoundation for future research.

Conclusion

We found that a classifier using probabilistic labels is effective for training an optimal CNN classifier of oral
exfoliative cytology when using annotations provided by multiple pathologists. The resulting model may serve asa trusted medical artificial intelligence solution that reflects diverse diagnoses provided by various pathologists.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Received: 2 April 2024; Accepted: 17 July 2024

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Acknowledgements

This work was indirectly supported by JST CREST (JPMJCR21D4), Japan.

Author contributions

The study was conceived by S.S. and T.H., who also conducted the experiments. F.T., Y.I., and Y.T. conducted
the experiments. K.N., T.O., and S.K. generated the data. All authors analyzed and interpreted the data. S.S. and
T.H. wrote the manuscript. F.M., Y.N., T.I., R.M., S.M., H.N. and M.M. revised the manuscript. All authors readand approved the final version of the manuscript.

Competing interests

The authors declare no competing interests.
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Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​
10.​1038/​s41598-​024-​67879-w.
Correspondence and requests for materials should be addressed to S.S.
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