Abstract book (pdf) - ICPR 2010

in specific brain regions. Moreover, significant links were confined to the right brain hemisphere in FTLD, consistent with
the clinical symptoms. Considering multimodality effect size between bvFTD patients and controls, brain atrophy and hypoperfusion
regions identified by multimodality jICA yielded the large effect size while regions identified by unimodality
analysis of atrophy and hypoperfusion differences revealed only a medium multimodality effect size between bvFTD patients
and controls. The findings demonstrate the power of jICA to effectively evaluate multimodality brain imaging data.
13:50-14:10, Paper WeBT7.2
Endoscopic Image Classification using Edge-Based Features
Häfner, Michael, St. Elisabeth Hospital
Gangl, Alfred, Medical Univ. of Vienna
Liedlgruber, Michael, Univ. of Salzburg
Uhl, Andreas, Univ. of Salzburg
Vécsei, Andreas, St. Anna Children’s Hospital
Wrba, Friedrich, Medical Univ. of Vienna
We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous
work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges.
To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification
is then carried out using the k-nearest neighbors (k-NN) classifier. The results obtained are very promising and show that
an automated classification of the given imagery is feasible by using the proposed method.
14:10-14:30, Paper WeBT7.3
Biclustering of Expression Microarray Data with Topic Models
Bicego, Manuele, Univ. of Verona
Lovato, Pietro, Univ. of Verona
Ferrarini, Alberto, Univ. of Verona
Delledonne, Massimo, Univ. of Verona
This paper presents an approach to extract biclusters from expression micro array data using topic models a class of probabilistic
models which allow to detect interpretable groups of highly correlated genes and samples. Starting from a topic
model learned from the expression matrix, some automatic rules to extract biclusters are presented, which overcome the
drawbacks of previous approaches. The methodology has been positively tested with synthetic benchmarks, as well as
with a real experiment involving two different species of grape plants (Vitis vinifera and Vitis riparia).
14:30-14:50, Paper WeBT7.4
A Multiple Instance Learning Approach Toward Optimal Classification of Pathology Slides
Dundar, Murat, IUPUI
Badve, Sunil, Indiana Univ.
Raykar, Vikas, Siemens Medical
Jain, Rohit, IUPUI
Sertel, Olcay, The Ohio State Univ.
Gurcan, Metin, The Ohio State Univ.
Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified
on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is
confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has
to rule out cancer for each and every ROI available. Our research is motivated toward computer-assisted classification of
digitized slides. The objective in this study is to develop a classifier to optimize classification accuracy at the slide level.
Traditional supervised training techniques which are trained to optimize classifier performance at the ROI level yield suboptimal
performance in this problem. We propose a multiple instance learning approach based on the implementation of
the large margin principle with different loss functions defined for positive and negative samples. We consider the classification
of intraductal breast lesions as a case study, and perform experimental studies comparing our approach against
the state-of-the-art.
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