In the upcoming winter term (starting on October 2), I will give the following courses at AAU:

• 620.005 UE Introduction to Computer Science (Part 1, German)
• 620.025 UE Introduction to Computer Science (Part 2, German)
• 621.703 PR Computer Organization (German)

Access to course material in Moodle is restricted to enrolled students, but can also be requested from me by e-mail.

I successfully defended my PhD thesis on May 17, 2017.

For download: slides of my PhD presentation and supplemental slide set.

My PhD thesis has been submitted on April 6 and graded as excellent (grade 1).

Title of thesis: Concept-Based and Multimodal Methods for Medical Case Retrieval

Abstract:
Medical case retrieval (MCR) is defined as a multimedia retrieval problem, where the document collection consists of medical case descriptions that pertain to particular diseases, patients’ histories, or other entities of biomedical knowledge. Case descriptions are multimedia documents containing textual and visual modalities (images). A query may consist of a textual description of patient’s symptoms and related diagnostic images. This thesis proposes and evaluates methods that aim at improving MCR effectiveness over the baseline of fulltext retrieval. We hypothesize that this objective can be achieved by utilizing controlled vocabularies of biomedical concepts for query expansion and concept-based retrieval. The latter represents case descriptions and queries as vectors of biomedical concepts, which may be generated automatically from textual and/or visual modalities by concept mapping algorithms. We propose a multimodal retrieval framework for MCR by late fusion of text-based retrieval (including query expansion) and concept-based retrieval and show that retrieval effectiveness can be improved by 49% using linear fusion of practical component retrieval systems. The potential of further improvement is experimentally estimated as a 166% increase of effectiveness over fulltext retrieval using query-adaptive fusion of ideal component retrieval systems. Additional contributions of this thesis include the proposal and comparative evaluation of methods for concept mapping, query and document expansion, and automatic classification and separation of compound figures found in case descriptions.

Keywords: multimedia information retrieval / biomedical information retrieval / biomedical concept detection / information fusion / image processing

Bibtex citation:

@PhdThesis{Taschwer2017,
Title                    = {Concept-Based and Multimodal Methods for Medical Case Retrieval},
Author                   = {Taschwer, Mario W.},
School                   = {Alpen-Adria-Universit{\"a}t Klagenfurt},
Year                     = {2017},
Address                  = {Austria},
Month                    = mar,
Url                      = {http://www.itec.aau.at/bib/files/phd-thesis-taschwer.pdf}
}

In the upcoming summer term (starting on March 1), I will give the following courses at AAU:

• 620.002 Introduction to Computer Science (Exercises, German)
• 621.401 Compiler Construction (Lab, English)

We submitted extended work on compound figure separation to the MTAP Journal.

Update: The revised version of our paper has been accepted for publication on Dec 1, 2016 and published online on Dec 29, 2016. The printed version appeared in January, 2018.

Title: Automatic Separation of Compound Figures in Scientific Articles

Abstract:
Content-based analysis and retrieval of digital images found in scientific articles is often hindered by images consisting of multiple subfigures (compound figures). We address this problem by proposing a method (ComFig) to automatically classify and separate compound figures, which consists of two main steps: (i) a supervised compound figure classifier (ComFig classifier) discriminates between compound and non-compound figures using task-specific image features; and (ii) an image processing algorithm is applied to predicted compound images to perform compound figure separation (ComFig separation). The proposed ComFig classifier is shown to achieve state-of-the-art classification performance on a published dataset. Our ComFig separation algorithm shows superior separation accuracy on two different datasets compared to other known automatic approaches. Finally, we propose a method to evaluate the effectiveness of the ComFig chain combining classifier and separation algorithm, and use it to optimize the misclassification loss of the ComFig classifier for maximal effectiveness in the chain.

DOI: https://doi.org/10.1007/s11042-016-4237-x

Bibtex citation:

@Article{Taschwer2018,
  Title                    = {Automatic separation of compound figures in scientific articles},
  Author                   = {Taschwer, Mario and Marques, Oge},
  Journal                  = {Multimedia Tools and Applications},
  Year                     = {2018},
  Month                    = {Jan},
  Number                   = {1},
  Pages                    = {519--548},
  Volume                   = {77},
  Doi                      = {10.1007/s11042-016-4237-x},
  ISSN                     = {1573-7721}
}

In the upcoming summer term (starting on March 1, 2016), I will give the following two courses:

• 620.002 Introduction to computer science (exercises, German)
• 621.401 Compiler construction (lab, English)

Course material will be provided for students in non-public Moodle. If you are not enrolled to these courses and interested in the course material, please drop me an e-mail.

Our extended work on compound figure separation has been accepted as a regular paper at MMM 2016 conference. A preprint is available here.

Update: Slides of my presentation on Jan 5, 2016 at MMM conference. Official link to published paper. BibTeX citation:

@InCollection{Taschwer2016,
Title                    = {Compound Figure Separation Combining Edge and Band Separator Detection},
Author                   = {Taschwer, Mario and Marques, Oge},
Booktitle                = {MultiMedia Modeling},
Publisher                = {Springer International Publishing},
Year                     = {2016},
Editor                   = {Tian, Qi and Sebe, Nicu and Qi, Guo-Jun and Huet, Benoit and Hong, Richang and Liu, Xueliang},
Pages                    = {162--173},
Series                   = {Lecture Notes in Computer Science},
Volume                   = {9516},

Doi                      = {10.1007/978-3-319-27671-7_14},
ISBN                     = {978-3-319-27670-0}
}

Abstract:
We propose an image processing algorithm to automatically separate compound figures appearing in scientific articles. We classify compound images into two classes and apply different algorithms for detecting vertical and horizontal separators to each class: the edge-based algorithm aims at detecting visible edges between subfigures, whereas the band-based algorithm tries to detect whitespace separating subfigures (separator bands). The proposed algorithm has been evaluated on two recent datasets for compound figure separation (CFS) in the biomedical domain and achieves a slightly better detection accuracy than state-of-the-art approaches. Conducted experiments investigate CFS effectiveness and classification accuracy of various classifier implementations.

We participated in the compound figure separation subtask of the ImageCLEF 2015 medical classification task (group AAUITEC). The paper describing our approach has been accepted for the CLEF 2015 Working Notes. A preprint version is available here.

Update: I presented a poster at CLEF 2015 in Toulouse on September 9, 2015. The paper appeared online in CEUR Workshop Proceedings. Here is the BibTeX citation:

@InProceedings{Taschwer2015,
Title                    = {{AAUITEC} at {ImageCLEF} 2015: Compound Figure Separation},
Author                   = {Taschwer, Mario and Marques, Oge},
Booktitle                = {{CLEF} 2015 Working Notes},
Year                     = {2015},
Month                    = {September},
Series                   = {CEUR Workshop Proceedings, ISSN 1613-0073},
Volume                   = {1391},
Location                 = {Toulouse, France},
Url                      = {http://ceur-ws.org/Vol-1391/25-CR.pdf}
}

Abstract:
Our approach to automatically separating compound figures appearing in biomedical articles is split into two image processing algorithms: one is based on detecting separator edges, and the other tries to identify background bands separating subfigures. Only one algorithm is applied to a given image, according to the prediction of a binary classifier trained to distinguish graphical illustrations from other images in biomedical articles. Our submission to the ImageCLEF 2015 compound figure separation task achieved an accuracy of 49% on the provided test set of about 3400 compound images. This stays clearly behind the best submission of other participants (85% accuracy), but is by an order of magnitude faster than other approaches reported in the literature.