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    <publishedYear>2021</publishedYear>
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    <title language="eng">Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades</title>
    <parentTitle language="eng">Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021: Proceedings</parentTitle>
    <identifier type="isbn">978-3-030-73973-7</identifier>
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    <author>
      <first_name>Peter</first_name>
      <last_name>Bellmann</last_name>
    </author>
    <editor>
      <first_name>Andrea</first_name>
      <last_name>Torsello</last_name>
    </editor>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Luca</first_name>
      <last_name>Rossi</last_name>
    </editor>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <editor>
      <first_name>Marcello</first_name>
      <last_name>Pelillo</last_name>
    </editor>
    <editor>
      <first_name>Battista</first_name>
      <last_name>Biggio</last_name>
    </editor>
    <editor>
      <first_name>Antonio</first_name>
      <last_name>Robles-Kelly</last_name>
    </editor>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ordinal classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ordinal classifier cascades</value>
    </subject>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
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    <title language="eng">Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario</title>
    <parentTitle language="eng">Pattern Recognition: ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021: Proceedings, Part VI</parentTitle>
    <identifier type="isbn">978-3-030-68780-9</identifier>
    <identifier type="isbn">978-3-030-68779-3</identifier>
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    <author>
      <first_name>Peter</first_name>
      <last_name>Bellmann</last_name>
    </author>
    <editor>
      <first_name>Alberto</first_name>
      <last_name>Del Bimbo</last_name>
    </editor>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Rita</first_name>
      <last_name>Cucchiara</last_name>
    </editor>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <editor>
      <first_name>Stan</first_name>
      <last_name>Sclaroff</last_name>
    </editor>
    <author>
      <first_name>Friedhelm</first_name>
      <last_name>Schwenker</last_name>
    </author>
    <editor>
      <first_name>Giovanni Maria</first_name>
      <last_name>Farinella</last_name>
    </editor>
    <editor>
      <first_name>Tao</first_name>
      <last_name>Mei</last_name>
    </editor>
    <editor>
      <first_name>Marco</first_name>
      <last_name>Bertini</last_name>
    </editor>
    <editor>
      <first_name>Hugo Jair</first_name>
      <last_name>Escalante</last_name>
    </editor>
    <editor>
      <first_name>Roberto</first_name>
      <last_name>Vezzani</last_name>
    </editor>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ordinal classifier cascades</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ordinal classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pain intensity recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physiological signals</value>
    </subject>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
  </doc>
  <doc>
    <id>3210</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue/>
    <volume>6</volume>
    <articleNumber>1056422</articleNumber>
    <type>article</type>
    <publisherName>Frontiers Media</publisherName>
    <publisherPlace>Lausanne</publisherPlace>
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    <completedDate>--</completedDate>
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    <title language="eng">Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images</title>
    <abstract language="eng">In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.</abstract>
    <parentTitle language="eng">Frontiers in Artificial Intelligence</parentTitle>
    <identifier type="issn">2624-8212</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-32108</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3389/frai.2023.1056422</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Patrick</first_name>
      <last_name>Thiam</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Christopher</first_name>
      <last_name>Kloth</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Blaich</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Liebold</last_name>
    </author>
    <author>
      <first_name>Meinrad</first_name>
      <last_name>Beer</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>chest X-ray</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cardiomegaly</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>transfer learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>unsupervised domain adaptation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3210/frai-06-1056422.pdf</file>
  </doc>
  <doc>
    <id>4900</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>19</volume>
    <articleNumber>e0304324</articleNumber>
    <type>article</type>
    <publisherName>PLOS</publisherName>
    <publisherPlace>San Francisco</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-07-15</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial</title>
    <abstract language="eng">Background&#13;
Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.&#13;
&#13;
Patients and methods&#13;
15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).&#13;
&#13;
Results&#13;
Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.&#13;
&#13;
Conclusions&#13;
We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.</abstract>
    <parentTitle language="eng">PLOS ONE</parentTitle>
    <identifier type="issn">1932-6203</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-49003</identifier>
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So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.&lt;\/jats:p&gt;\n&lt;\/jats:sec&gt;\n&lt;jats:sec id=\"sec002\"&gt;\n&lt;jats:title&gt;Patients and methods&lt;\/jats:title&gt;\n&lt;jats:p&gt;15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. 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    <title language="eng">Permutation-invariant linear classifiers</title>
    <abstract language="eng">Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker.</abstract>
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    <title language="eng">Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation</title>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Thomas Martin</first_name>
      <last_name>Deserno</last_name>
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      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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      <first_name>Heinz</first_name>
      <last_name>Handels</last_name>
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      <first_name>Robert</first_name>
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      <first_name>Marc</first_name>
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      <first_name>Klaus H.</first_name>
      <last_name>Maier-Hein</last_name>
    </editor>
    <editor>
      <first_name>Christoph</first_name>
      <last_name>Palm</last_name>
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    <editor>
      <first_name>Thomas</first_name>
      <last_name>Tolxdorff</last_name>
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    <title language="eng">A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells</title>
    <abstract language="eng">Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.</abstract>
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      <first_name>Lea</first_name>
      <last_name>Kubis</last_name>
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      <first_name>Julian D.</first_name>
      <last_name>Schwab</last_name>
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    <author>
      <first_name>Silke D.</first_name>
      <last_name>Werle</last_name>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
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    <author>
      <first_name>Stefan</first_name>
      <last_name>Tümpel</last_name>
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    <author>
      <first_name>Astrid S.</first_name>
      <last_name>Pfister</last_name>
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    <author>
      <first_name>Michael</first_name>
      <last_name>Kühl</last_name>
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      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
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    <title language="eng">Benchmarking Foundation Models for Mitotic Figure Classification</title>
    <abstract language="eng">The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 % data availability with only 10 % of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance.</abstract>
    <parentTitle language="eng">Machine Learning for Biomedical Imaging</parentTitle>
    <identifier type="issn">2766-905X</identifier>
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