1 code implementation • 21 Mar 2021 • Gijs van Tulder, Yao Tong, Elena Marchiori
We present a novel cross-view transformer method to transfer information between unregistered views at the level of spatial feature maps.
no code implementations • 8 Nov 2020 • Kai Chen, Twan van Laarhoven, Elena Marchiori
The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain.
no code implementations • 6 May 2019 • Sebastiano Vascon, Sinem Aslan, Alessandro Torcinovich, Twan van Laarhoven, Elena Marchiori, Marcello Pelillo
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
no code implementations • 14 Aug 2018 • Joris van Vugt, Elena Marchiori, Ritse Mann, Albert Gubern-Mérida, Nikita Moriakov, Jonas Teuwen
We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available.
no code implementations • 7 Aug 2018 • Kai Chen, Twan van Laarhoven, Perry Groot, Jinsong Chen, Elena Marchiori
The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel.
no code implementations • 3 Aug 2018 • Kai Chen, Twan van Laarhoven, Elena Marchiori, Feng Yin, Shuguang Cui
The function interaction is modeled by using cross convolution of latent functions.
no code implementations • 1 Aug 2018 • Kai Chen, Yijue Dai, Feng Yin, Elena Marchiori, Sergios Theodoridis
Then, we propose a novel SM kernel with a dependency structure (SMD) by using cross-convolution between the SM components.
no code implementations • 12 Apr 2018 • Jeroen Manders, Twan van Laarhoven, Elena Marchiori
Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level.
1 code implementation • 20 Mar 2018 • Twan van Laarhoven, Elena Marchiori
Despite their success, state of the art methods based on this approach are either involved or unable to directly scale to data with many features.
1 code implementation • 15 Nov 2017 • Jacopo Acquarelli, Elena Marchiori, Lutgarde M. C. Buydens, Thanh Tran, Twan van Laarhoven
2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets?
no code implementations • 14 Sep 2017 • Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello
Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors.
no code implementations • 16 Jun 2017 • Twan van Laarhoven, Elena Marchiori
Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain.
no code implementations • 25 Feb 2017 • Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
no code implementations • 24 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
no code implementations • 16 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
no code implementations • 21 Jan 2016 • Twan van Laarhoven, Elena Marchiori
We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance.
no code implementations • 22 Jul 2014 • Twan van Laarhoven, Elena Marchiori
We argue that this is a desirable property, provide conditions under which NMF quality functions are local, and propose a novel class of local probabilistic NMF quality functions for soft graph clustering.
no code implementations • 15 Aug 2013 • Twan van Laarhoven, Elena Marchiori
This motivates the derivation of a new family of quality functions, adaptive scale modularity, which does satisfy the proposed axioms.