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    <title language="eng">On the Value of PHH3 for Mitotic Figure Detection on H&amp;E-stained Images</title>
    <abstract language="eng">The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&amp;E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. Deep learning algorithms can standardize this task, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithm's performance. Unlike H&amp;E, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;E stain alone, the use of this ground truth could potentially introduce noise into the H&amp;E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. We found that the annotators' object-level agreement increased when using PHH3-assisted labeling. Subsequently, MF detectors were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. Additionally, a novel dual-stain MF detector was developed to investigate the interpretation-shift of PHH3-assisted labels used in H&amp;E, which clearly outperformed single-stain detectors. However, the PHH3-assisted labels did not have a positive effect on solely H&amp;E-based models. The high performance of our dual-input detector reveals an information mismatch between the H&amp;E and PHH3-stained images as the cause of this effect.</abstract>
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      <first_name>Matthew J.</first_name>
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      <first_name>Christof</first_name>
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    <title language="eng">Information Mismatch in PHH3-Assisted Mitosis Annotation Leads to Interpretation Shifts in H&amp;E Slide Analysis</title>
    <abstract language="eng">The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&amp;E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms' performance. Unlike H&amp;E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&amp;E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance.&#13;
&#13;
We found that the annotators' object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&amp;E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&amp;E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&amp;E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.</abstract>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
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      <first_name>Barbara</first_name>
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      <first_name>Chloé</first_name>
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      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
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      <first_name>Elena A.</first_name>
      <last_name>Demeter</last_name>
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      <first_name>Flaviu A.</first_name>
      <last_name>Tabaran</last_name>
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      <first_name>Gabriel</first_name>
      <last_name>Wasinger</last_name>
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      <first_name>Karoline</first_name>
      <last_name>Lipnik</last_name>
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      <first_name>Marco</first_name>
      <last_name>Tecilla</last_name>
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    <author>
      <first_name>Matthew J.</first_name>
      <last_name>Valentine</last_name>
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      <first_name>Michael</first_name>
      <last_name>Dark</last_name>
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    <title language="eng">Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing</title>
    <abstract language="eng">AbstractHistopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.</abstract>
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    <title language="eng">Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&amp;E slide analysis</title>
    <abstract language="eng">The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&amp;E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&amp;E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&amp;E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&amp;E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&amp;E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&amp;E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms\u2019 performance. Unlike H&amp;amp;E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&amp;amp;E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&amp;amp;E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&amp;amp;E-related dataset, impacting model performance. 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