Machine learning approaches in medical image analysis: From detection to diagnosis

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results

Effects of aquatic vegetation type on denitrification

In a microcosm 15N enrichment experiment we tested the effect of floating vegetation (Lemna sp.) and submerged vegetation (Elodea nuttallii) on denitrification rates, and compared it to systems without macrophytes. Oxygen concentration, and thus photosynthesis, plays an important role in regulating denitrification rates and therefore the experiments were performed under dark as well as under light conditions. Denitrification rates differed widely between treatments, ranging from 2.8 to 20.9 µmol N m-2 h-1, and were strongly affected by the type of macrophytes present. These differences may be explained by the effects of macrophytes on oxygen conditions. Highest denitrification rates were observed under a closed mat of floating macrophytes where oxygen concentrations were low. In the light, denitrification was inhibited by oxygen from photosynthesis by submerged macrophytes, and by benthic algae in the systems without macrophytes. However, in microcosms with floating vegetation there was no effect of light, as the closed mat of floating plants caused permanently dark conditions in the water column. Nitrate removal was dominated by plant uptake rather than denitrification, and did not differ between systems with submerged or floating plant

Temperature determination via STJ optical spectroscopy

ESA's Superconducting Tunnel Junction (STJ) optical photon-counting camera (S-Cam2) incorporates an array of pixels with intrinsic energy sensitivity. Using the spectral fitting technique common in X-ray astronomy, we fit black bodies to nine stellar spectra, ranging from cool flare stars to hot white dwarfs. The measured temperatures are consistent with literature values at the expected level of accuracy based on the predicted gain stability of the instrument. Having also demonstrated that systematic effects due to count rate are likely to be small, we then proceed to apply the temperature determination method to four cataclysmic variable (CV) binary systems. In three cases we measure the temperature of the accretion stream, while in the fourth we measure the temperature of the white dwarf. The results are discussed in the context of existing CV results. We conclude by outlining the prospects for future versions of S-Cam.Comment: 9 pages, 9 figures (11 files); uses aa.cls; accepted for publication in A&

Why Does Synthesized Data Improve Multi-sequence Classification?

The classification and registration of incomplete multi-modal medical images, such as multi-sequence MRI with missing sequences, can sometimes be improved by replacing the missing modalities with synthetic data. This may seem counter-intuitive: synthetic data is derived from data that is already available, so it does not add new information. Why can it still improve performance? In this paper we discuss possible explanations. If the synthesis model is more flexible than the classifier, the synthesis model can provide features that the classifier could not have extracted from the original data. In addition, using synthetic information to complete incomplete samples increases the size of the training set. We present experiments with two classifiers, linear support vector machines (SVMs) and random forests, together with two synthesis methods that can replace missing data in an image classification problem: neural networks and restricted Boltzmann machines (RBMs). We used data from the BRATS 2013 brain tumor segmentation challenge, which includes multi-modal MRI scans with T1, T1 post-contrast, T2 and FLAIR sequences. The linear SVMs appear to benefit from the complex transformations offered by the synthesis models, whereas the random forests mostly benefit from having more training data. Training on the hidden representation from the RBM brought the accuracy of the linear SVMs close to that of random forests

Detecting stars, galaxies, and asteroids with Gaia

(Abridged) Gaia aims to make a 3-dimensional map of 1,000 million stars in our Milky Way to unravel its kinematical, dynamical, and chemical structure and evolution. Gaia's on-board detection software discriminates stars from spurious objects like cosmic rays and Solar protons. For this, parametrised point-spread-function-shape criteria are used. This study aims to provide an optimum set of parameters for these filters. We developed an emulation of the on-board detection software, which has 20 free, so-called rejection parameters which govern the boundaries between stars on the one hand and sharp or extended events on the other hand. We evaluate the detection and rejection performance of the algorithm using catalogues of simulated single stars, double stars, cosmic rays, Solar protons, unresolved galaxies, and asteroids. We optimised the rejection parameters, improving - with respect to the functional baseline - the detection performance of single and double stars, while, at the same time, improving the rejection performance of cosmic rays and of Solar protons. We find that the minimum separation to resolve a close, equal-brightness double star is 0.23 arcsec in the along-scan and 0.70 arcsec in the across-scan direction, independent of the brightness of the primary. We find that, whereas the optimised rejection parameters have no significant impact on the detectability of de Vaucouleurs profiles, they do significantly improve the detection of exponential-disk profiles. We also find that the optimised rejection parameters provide detection gains for asteroids fainter than 20 mag and for fast-moving near-Earth objects fainter than 18 mag, albeit this gain comes at the expense of a modest detection-probability loss for bright, fast-moving near-Earth objects. The major side effect of the optimised parameters is that spurious ghosts in the wings of bright stars essentially pass unfiltered.Comment: Accepted for publication in A&

Variability of the Accretion Stream in the Eclipsing Polar EP Dra

We present the first high time resolution light curves for six eclipses of the magnetic cataclysmic variable EP Dra, taken using the superconducting tunnel junction imager S-Cam2. The system shows a varying eclipse profile between consecutive eclipses over the two nights of observation. We attribute the variable stream eclipse after accretion region ingress to a variation in the amount and location of bright material in the accretion stream. This material creates an accretion curtain as it is threaded by many field lines along the accretion stream trajectory. We identify this as the cause of absorption evident in the light curves when the system is in a high accretion state. We do not see direct evidence in the light curves for an accretion spot on the white dwarf; however, the variation of the stream brightness with the brightness of the rapid decline in flux at eclipse ingress indicates the presence of some form of accretion region. This accretion region is most likely located at high colatitude on the white dwarf surface, forming an arc shape at the foot points of the many field lines channeling the accretion curtain.Comment: Accepted for publication in MNRAS (7 pages