Termine an der TF

Sonderkolloquium, Jun.-Prof. Dr. Mattias Heinrich, Uni Lübeck / am 07.07.2017

07.07.2017 von 14:15 bis 15:45

Institut für Informatik,Christian-Albrechts-Platz 4, R.715, 24114 Kiel

Titel: Learning Sparse Binary Features for Medical Image Segmentation of the Abdomen

Abstract: In this talk, we explore the capabilities of sparse binary features for medical image segmentation. Due to insufficient contrast and anatomical shape variations local image patches rarely provide sufficient information for accurate segmentation of abdominal structures. Based on our two recent MICCAI papers, we propose to use long-range binary features to robustly capture the image context. Two different classification strategies are subsequently developed. 

First, a very fast approximate nearest neighbour search based on vantage point forests and Hamming distances between feature strings is presented. The classifier can be learned and applied to new data in few seconds. The approach reaches state-of-the-art performance for larger organs on the VISCERAL3 benchmark.

Second, we develop a deep neural network architecture that combines a local CNN path with a new contextual path that encodes the sparse binary features. Following the ideas from Network-in-Network, 1x1 convolutions are employed to learn the best combination of different binary offset locations. We demonstrate experimentally that this restricted feature extraction in the first layer enables to regularise the network with a huge receptive field and leads to short training times of less than 10 minutes. Using only 1 million trainable parameters, the model achieves a accuracy of 64.5% Dice, which is comparable to the best performing, much more complex deep CNN approach for pancreas segmentation.

Finally, the potential use of learned binary features for other tasks in medical image analysis, such as image registration and disease classification will be discussed.

Prof. Carsten Meyer

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Kolloquiumsvortrag, Prof. Grandoni, IDSIA USI-SUPSI in Lugano / am 07.07.2017

07.07.2017 von 14:15 bis 15:45

Institut für Informatik, Ludewig-Meyn-Str. 2, Raum Ü2/K (LMS2, R. Ü2/K), 24114 Kiel

Titel: Approximating Geometric Knapsack via L-packings

Abstract: Joint work with: Waldo Galvez, Sandy Heydrich, Salvatore Ingala,
Arindam Khan, Andreas Wiese

In the 2-dimensional geometric knapsack problem (2DK) we are given a
set of n axis-aligned rectangular items, each one with an associated
profit, and an axis-aligned square knapsack. The goal is to find a
(non-overlapping) packing of a maximum profit subset of items inside
the knapsack (without rotating items). The best-known polynomial-time
approximation factor for this problem (even just in the cardinality
case) is 2 + ε [Jansen and Zhang, SODA 2004]. In this work we break
the 2 approximation barrier, achieving a polynomial-time 17/9 + ε <
1.89 approximation, which improves to 558/325+ ε < 1.72 in the
cardinality case.

Essentially all prior work on 2DK approximation packs items inside a
constant number of rectangular containers, where items inside each
container are packed using a simple greedy strategy. We deviate for
the first time from this setting: we show that there exists a large
profit solution where items are packed inside a constant number of
containers plus one L-shaped region at the boundary of the knapsack
which contains items that are high and narrow and items that are wide
and thin. The items of these two types possibly interact in a complex
manner at the corner of the L.

The above structural result is not enough however: the best-known
approximation ratio for the sub-problem in the L-shaped region is 2 +
ε (obtained via a trivial reduction to 1-dimensional knapsack by
considering tall or wide items only). Indeed this is one of the
simplest special settings of the problem for which this is the best
known approximation factor. As a second major, and the main
algorithmic contribution of this work, we present a PTAS for this
case. We believe that this will turn out to be useful in future work
in geometric packing problems.

We also consider the variant of the problem with rotations (2DKR),
where items can be rotated by 90 degrees. Also in this case the
best-known polynomial-time approximation factor (even for the
cardinality case) is 2 + ε [Jansen and Zhang, SODA 2004]. Exploiting
part of the machinery developed for 2DK plus a few additional ideas,
we obtain a polynomial-time 3/2 + ε-approximation for 2DKR, which
improves to 4/3 + ε in the cardinality case.

Prof. Jansen

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