MediaEval 2018 Medico Task

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We participated in the MediaEval 2018 Medico task and recently submitted our working notes paper. This is joint work with Oge Marques (Florida Atlantic University, USA).

Update: Our paper has been accepted and presented at the MediaEval Workshop on Oct 30, 2018.

Title: Early and Late Fusion of Classifiers for the MediaEval Medico Task

Authors: Mario Taschwer, Manfred Jürgen Primus, Klaus Schoeffmann, Oge Marques

Abstract: We present our results for the MediaEval 2018 Medico
task, achieved with traditional machine learning methods, such as
logistic regression, support vector machines, and random forests.
Before classification, we combine traditional global image features
and CNN-based features (early fusion), and apply soft voting for
combining the output of multiple classifiers (late fusion). Linear
support vector machines turn out to provide both good classification
performance and low run-time complexity for this task.

Paper: [Preprint PDF]

Presentation: [Slides PDF]

Course in winter term 2018/19

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In the upcoming winter term (starting in October 2018), I’ll give a single lab course:

621.703 Computer Organization (PR Rechnerorganisation)

Information about the course (modalities, grading, course material) will be provided in non-public Moodle, accessible by attendees after the first class meeting.

OVID – Relevance Detection in Ophthalmic Surgery Videos

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Our FWF research grant proposal OVID (Relevance Detection in Ophthalmic Surgery Videos) has recently been approved! The research project will start in fall 2018 and last for 3 years (3 PhD positions, 1 student assistant – applications are welcome!). The project will be conducted in cooperation with Klinikum Klagenfurt.

Authors: Klaus Schoeffmann, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, Yosuf El-Shabrawi, Laszlo Böszörmenyi

Abstract:

In this project, we want to investigate fundamental research questions in the field of postoperative analysis of ophthalmic surgery (i.e. concerned with the human eye) videos (OSVs). More precisely, three research objectives are covered: (1) Classification of OSV segments – is it possible to improve upon the state-of-the-art in automatic content classification and content segmentation of OSVs, focusing on regular and irregular operation phases? (2) Relevance prediction and relevance-driven compression – how accurately can the relevance of OSV segments be determined automatically for educational, scientific, and documentary purposes (as medical experts would do), and what compression efficiency can be achieved for OSVs when considering relevance as an additional modality? (3) Analysis of common irregularities in OSVs for medical research – we address three quantitative medical research questions related to cataract surgeries, such as: is there a statistically significant difference in duration or complication rate between cataract surgeries showing intraoperative pupil reactions and those showing no such pupil reactions?

We plan to perform these investigations using data acquisition, data modelling, video content analysis, statistical analysis, and state-of-the-art machine learning methods – such as content classifiers based on deep learning. The proposed methods will be evaluated on annotated video datasets (“ground truth”) created by medical field experts during the project.

Beyond developing novel methods for solving the abovementioned research problems, project results are expected to have innovative effects in the emerging interdisciplinary field of automatic video-based analysis of ophthalmic surgeries. In particular, research results of this project will enable efficient permanent video documentation of ophthalmic surgeries, allowing to create OSV datasets relevant for medical education, training, and research. Moreover, archives of relevant OSVs will enable novel postoperative analysis methods for medical research questions – such as causes for irregular operation phases, for example.

The research project will be a cooperation between computer scientists of AAU Klagenfurt (conducted by Prof. Klaus Schöffmann, supported and advised by Dr. Mario Taschwer and Prof. Laszlo Böszörmenyi) and ophthalmic surgeons and researchers at Klinikum Klagenfurt (Dr. Doris Putzgruber-Adamitsch, Dr. Stephanie Sarny, Prof. Yosuf El-Shabrawi).

Video Dataset of 101 Cataract Surgeries

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Our paper “Cataract-101 – Video Dataset of 101 Cataract Surgeries” has been accepted for poster presentation at MMSys 2018 conference (Open DataSet & Software Track).

Authors: Klaus Schoeffmann, Mario Taschwer, Stephanie Sarny, Bernd Münzer, Jürgen Primus, Doris Putzgruber

Abstract:
Cataract surgery is one of the most frequently performed microscopic surgeries in the field of ophthalmology. The goal behind this kind of surgery is to replace the human eye lense with an artificial one, an intervention that is often required due to aging. The entire surgery is performed under microscopy, but co-mounted cameras allow to record and archive the procedure. Currently, the recorded videos are used in a postoperative manner for documentation and training. An additional benefit of recording cataract videos is that they enable video analytics (i.e., manual and/or automatic video content analysis) to investigate medically relevant research questions (e.g., the cause of complications). This, however, necessitates a medical multimedia information system trained and evaluated on existing data, which is currently not publicly available. In this work we provide a public video dataset of 101 cataract surgeries that were performed by four different surgeons over a period of 9 months. These surgeons are grouped into moderately experienced and highly experienced surgeons (assistant vs. senior physicians), providing the basis for experience-based video analytics. All videos have been annotated with quasi-standardized operation phases by a senior ophthalmic surgeon.

Dataset: http://www.itec.aau.at/ftp/datasets/ovid/cat-101/

DOI: https://doi.org/10.1145/3204949.3208137

[Preprint PDF] [Poster]

Erratum: Table 1 of the published paper contains a systematic error in the row titled “Avg. Length / Op”. Numbers have been corrected in the poster.

Classification of Operation Phases in Cataract Surgery Videos

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Our paper has been accepted for publication and oral presentation at MMM 2018 conference:

Title: Frame-Based Classification of Operation Phases in Cataract Surgery Videos

Authors: Manfred Jürgen Primus, Doris Putzgruber-Adamitsch, Mario Taschwer, Bernd Muenzer, Yosuf El-Shabrawi, Laszlo Boeszoermenyi and Klaus Schöffmann

Abstract: Cataract surgeries are frequently performed to correct a lens opacification of the human eye, which usually appears in the course of aging. These surgeries are conducted with the help of a microscope and are typically recorded on video for later inspection and educational purposes. However, post-hoc visual analysis of video recordings is cumbersome and time-consuming for surgeons if there is no navigation support, such as bookmarks to specific operation phases. To prepare the way for an automatic detection of operation phases in cataract surgery videos, we investigate the effectiveness of a deep convolutional neural network (CNN) to automatically assign video frames to operation phases, which can be regarded as a single-label multi-class classification problem. In absence of public datasets of cataract surgery videos, we provide a dataset of 21 videos of standardized cataract surgeries and use it to train and evaluate our CNN classifier. Experimental results display a mean F1-score of about 68% for frame-based operation phase classification, which can be further improved to 75% when considering temporal information of video frames in the CNN architecture.

Dataset: http://www.itec.aau.at/ftp/datasets/ovid/cat-21/

Preprint PDF
DOI:
https://doi.org/10.1007/978-3-319-73603-7_20

Bibtex:

@InProceedings{Primus2018,
  Title                    = {Frame-Based Classification of Operation Phases in Cataract Surgery Videos},
  Author                   = {Primus, Manfred J{\"u}ergen and Putzgruber-Adamitsch, Doris and Taschwer, Mario and M{\"u}nzer, Bernd and El-Shabrawi, Yosuf and B{\"o}sz{\"o}rmenyi, Laszlo and Schoeffmann, Klaus},
  Booktitle                = {MultiMedia Modeling},
  Year                     = {2018},

  Address                  = {Cham},
  Editor                   = {Schoeffmann, Klaus and Chalidabhongse, Thanarat H. and Ngo, Chong Wah and Aramvith, Supavadee and O'Connor, Noel E. and Ho, Yo-Sung and Gabbouj, Moncef and Elgammal, Ahmed},
  Pages                    = {241--253},
  Publisher                = {Springer International Publishing},
  ISBN                     = {978-3-319-73603-7}
}

Courses in winter term 2017

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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.

PhD thesis submitted

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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}
}

Courses in summer term 2017

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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)

Courses in winter term 2016

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Here are the courses I give this winter term, starting on October 3:

621.702 Computer organization (lab)
621.704 Computer organization (lab)

Students access the course material through non-public Moodle. If you are not enrolled to these courses but are interested in the course material (available in German only), please drop me an e-mail.