Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Blaschko, M."'
Autor:
Nguyen, H. H. (Huy Hoang), Saarakkala, S. (Simo), Blaschko, M. B. (Matthew B.), Tiulpin, A. (Aleksei)
In medical applications, deep learning methods are designed to automate diagnostic tasks. However, a clinically relevant question that practitioners usually face, is how to predict the future trajectory of a disease (prognosis). Current methods for s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::1c0c9231a7d61805af5d30c1da103e7b
http://urn.fi/urn:nbn:fi-fe2022101361858
http://urn.fi/urn:nbn:fi-fe2022101361858
Autor:
Geysels, A., Garofalo, G., Timmerman, S., Ceusters, J., Fischerová, D., Testa, A.C., Moro, F., Buonomo, F., Valentin, L., Sladkevicius, P., Van Holsbeke, C., Kudla, M.J., Czekierdowski, A., Epstein, E., Groszmann, Y., Blaschko, M., De Moor, B., Van Calster, B., Timmerman, D., Froyman, W.
Publikováno v:
Ultrasound in Obstetrics & Gynecology; Sep2024 Supplement 1, Vol. 64, p13-13, 1p
Autor:
Nguyen, H. H. (Huy Hoang), Saarakkala, S. (Simo), Blaschko, M. B. (Matthew B.), Tiulpin, A. (Aleksei)
Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiographic assessment. This visual assessment done by a radiologist requir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::be3ce599204a23b2c5728de81efa2f9c
http://urn.fi/urn:nbn:fi-fe202101192144
http://urn.fi/urn:nbn:fi-fe202101192144
Autor:
Thomas, S. S., Palandri, J., Lakehalayat, M., Chakravarty, P., Friedrich Wolf-Monheim, Blaschko, M. B.
Publikováno v:
Scopus-Elsevier
Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24f9eb7f8423ccf2262a6ebe42e7f315
https://lirias.kuleuven.be/handle/123456789/642720
https://lirias.kuleuven.be/handle/123456789/642720
Publikováno v:
ICCV
International Conference on Computer Vision
International Conference on Computer Vision, Nov 2011, Barcelona, Spain. pp.959-966, ⟨10.1109/ICCV.2011.6126339⟩
International Conference on Computer Vision
International Conference on Computer Vision, Nov 2011, Barcelona, Spain. pp.959-966, ⟨10.1109/ICCV.2011.6126339⟩
International audience; Equivariance and invariance are often desired properties of a computer vision system. However, currently available strategies generally rely on virtual sampling, leaving open the question of how many samples are necessary, on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4128cc841a7656984c26455d9ced77c1
https://doi.org/10.1109/iccv.2011.6126339
https://doi.org/10.1109/iccv.2011.6126339
Publikováno v:
Technical Report of the Max Planck Institute for Biological Cybernetics
Kernel Canonical Correlation Analysis is a very general technique for subspace learning that incorporates PCA and LDA as special cases. Functional magnetic resonance imaging (fMRI) acquired data is naturally amenable to these techniques as data are w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::839edd502b1fc926898b702c38e18d22
https://hdl.handle.net/21.11116/0000-0002-85B9-711858/00-001M-0000-0013-C509-D
https://hdl.handle.net/21.11116/0000-0002-85B9-711858/00-001M-0000-0013-C509-D
Autor:
Lampert, C., Blaschko, M.
Publikováno v:
NIPS 2008 Workshop: Structured Input-Structured Output (NIPS SISO 2008)
We present a new technique for structured prediction that works in a hybrid generative/ discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::a4e7920608b0eaaba1a8bbbf234f8bed
https://hdl.handle.net/11858/00-001M-0000-0013-C635-021.11116/0000-0003-378B-3
https://hdl.handle.net/11858/00-001M-0000-0013-C635-021.11116/0000-0003-378B-3
Autor:
Blaschko, M., Gretton, A.
Publikováno v:
Technical Report of the Max Planck Institute for Biological Cybernetics
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::8475d4dfd5287442d373b4df1ef9b728
https://hdl.handle.net/21.11116/0000-0002-8703-211858/00-001M-0000-0013-C673-4
https://hdl.handle.net/21.11116/0000-0002-8703-211858/00-001M-0000-0013-C673-4
Publikováno v:
Technical Report of the Max Planck Institute for Biological Cybernetics
Recent years have seen huge advances in object recognition from images. Recognition rates beyond 95 are the rule rather than the exception on many datasets. However, most state-of-the-art methods can only decide if an object is present or not. They a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::c5461ebd96d7ba5c42560ce068e7b03a
https://hdl.handle.net/11858/00-001M-0000-0013-CC4D-A21.11116/0000-0002-875A-1
https://hdl.handle.net/11858/00-001M-0000-0013-CC4D-A21.11116/0000-0002-875A-1
Autor:
Blaschko, M., Hofmann, T.
Publikováno v:
NIPS 2006 Workshop on Learning to Compare Examples
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::1a9324639da24d6ef595f25676bcfd8f
https://hdl.handle.net/11858/00-001M-0000-0013-CF47-F
https://hdl.handle.net/11858/00-001M-0000-0013-CF47-F