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  <doc>
    <id>8502</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>3315</volume>
    <type>conferenceobject</type>
    <publisherName>AIP Publishing</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
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    <completedDate>--</completedDate>
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    <title language="eng">BPConvNet: a deep learning based ρ-Filtered layergram reconstruction method for computed tomography</title>
    <abstract language="eng">In this article, we address the reconstruction problem in computed tomography (CT) when dealing with sparse view&#13;
data. Traditional approaches like filtered backprojection (FBP) often fail under these conditions, leading to streaking artifacts.&#13;
We propose BPConvNet, a deep learning based version of the ρ-filtered layergram or backprojection filtration (BPF) technique&#13;
(cf. [1]). Unlike FBP, the BPF method applies filtering (F) after backprojection (BP), hence the name. The proposed BPConvNet&#13;
adapts the BPF workflow by substituting the filtering step with a residual convolutional neural network. Our numerical experiments&#13;
demonstrate that BPConvNet is competitive to similar deep learning methods. Moreover, we explain that BPConvNet can be easily&#13;
adapted to to different CT acquisition geometries, such as fan beam and 3D configurations</abstract>
    <parentTitle language="eng">AIP Conference Proceedings</parentTitle>
    <identifier type="issn">0094-243X</identifier>
    <identifier type="doi">10.1063/5.0286063</identifier>
    <enrichment key="conference_title">International Conference of Numerical Analysis and Applied Mathematics (ICNAAM); 16-22 September 2025, Heraklion</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Patrick Bauer</author>
    <author>Jürgen Frikel</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning and learning models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computed tomography</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="DFGFachsystematik" number="4">Naturwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
  </doc>
  <doc>
    <id>7800</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection</title>
    <abstract language="eng">Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient’s clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient’s history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.</abstract>
    <parentTitle language="eng">2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Manaus, Brazil, 9/30/2024 - 10/3/2024</parentTitle>
    <identifier type="isbn">979-8-3503-7603-6</identifier>
    <identifier type="doi">10.1109/SIBGRAPI62404.2024.10716324</identifier>
    <enrichment key="opus.import.date">2024-11-18T10:16:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luis A. Souza</author>
    <author>André G.C. Pacheco</author>
    <author>Gabriel G. de Angelo</author>
    <author>Thiago Oliveira-Santos</author>
    <author>Christoph Palm</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lightweight Architectures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Trans- formers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Skin Lesion Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7308</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3355</pageFirst>
    <pageLast>3372</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>62</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-06-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Layer-selective deep representation to improve esophageal cancer classification</title>
    <abstract language="eng">Even though artiﬁcial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis.For this task, the deep learning techniques’ black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett’s esophagus and adenocarcinoma classiﬁcation. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classiﬁed for further deﬁnition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classiﬁcation for our task. Besides, we observed a signiﬁcant improvement when the most discriminative layers expressed more impact in the training and classiﬁcation of ResNet-50 for Barrett’s esophagus and adenocarcinoma classiﬁcation, demonstrating that both human knowledge and computational processing may inﬂuence the correct learning of such a problem.</abstract>
    <parentTitle language="eng">Medical &amp; Biological Engineering &amp; Computing</parentTitle>
    <identifier type="doi">10.1007/s11517-024-03142-8</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Leandro A. Passos</author>
    <author>Marcos Cleison S. Santana</author>
    <author>Robert Mendel</author>
    <author>David Rauber</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multistep training</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett’s esophagus detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>1461</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>June-August</issue>
    <volume>52-53</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Barrett esophagus: What to expect from Artificial Intelligence?</title>
    <abstract language="eng">The evaluation and assessment of Barrett’s esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett’s esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett’s esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett’s esophagus.</abstract>
    <parentTitle language="eng">Best Practice &amp; Research Clinical Gastroenterology</parentTitle>
    <identifier type="issn">1521-6918</identifier>
    <identifier type="doi">10.1016/j.bpg.2021.101726</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Computerunterstützte Medizin</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adenocarcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional neural networks</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>353</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>615</pageFirst>
    <pageLast>616</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>69</volume>
    <type>article</type>
    <publisherName>BMJ</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus</title>
    <abstract language="eng">Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.</abstract>
    <parentTitle language="eng">Gut</parentTitle>
    <identifier type="doi">10.1136/gutjnl-2019-319460</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Andreas Probst</author>
    <author>Johannes Manzeneder</author>
    <author>Friederike Prinz</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>real-time</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>354</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1616</pageFirst>
    <pageLast>1623</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>07</volume>
    <type>article</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology</title>
    <abstract language="eng">The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research.&#13;
&#13;
In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.&#13;
&#13;
The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians.&#13;
&#13;
This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.</abstract>
    <parentTitle language="eng">Endoscopy International Open</parentTitle>
    <identifier type="doi">10.1055/a-1010-5705</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Robert Mendel</author>
    <author>Johannes Manzeneder</author>
    <author>Friederike Prinz</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Peter Siersema</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gastroenterologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>98</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>475</pageFirst>
    <pageLast>485</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>59</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Barrett's esophagus analysis using infinity Restricted Boltzmann Machines</title>
    <abstract language="eng">The number of patients with Barret’s esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett’s esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.</abstract>
    <parentTitle language="eng">Journal of Visual Communication and Image Representation</parentTitle>
    <identifier type="doi">10.1016/j.jvcir.2019.01.043</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Leandro A. Passos</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Robert Mendel</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>Christoph Palm</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Boltzmann-Maschine</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett’s esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Infinity Restricted Boltzmann Machines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Meta-heuristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metaheuristik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
</export-example>
