Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN

Shearlet Features for Registration of Remotely Sensed Multitemporal Images

We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Experimental results on multitemporal Landsat images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms

Harvest-induced disruptive selection increases variance in fitness-related traits

The form of Darwinian selection has important ecological and management implications. Negative effects of harvesting are often ascribed to size truncation (i.e. strictly directional selection against large individuals) and resultant decrease in trait variability, which depresses capacity to buffer environmental change, hinders evolutionary rebound and ultimately impairs population recovery. However, the exact form of harvest-induced selection is generally unknown and the effects of harvest on trait variability remain unexplored. Here we use unique data from the Windermere (UK) long-term ecological experiment to show in a top predator (pike, Esox lucius) that the fishery does not induce size truncation but disruptive (diversifying) selection, and does not decrease but rather increases variability in pike somatic growth rate and size at age. This result is supported by complementary modelling approaches removing the effects of catch selectivity, selection prior to the catch and environmental variation. Therefore, fishing most likely increased genetic variability for somatic growth in pike and presumably favoured an observed rapid evolutionary rebound after fishery relaxation. Inference about the mechanisms through which harvesting negatively affects population numbers and recovery should systematically be based on a measure of the exact form of selection. From a management perspective, disruptive harvesting necessitates combining a preservation of large individuals with moderate exploitation rates, and thus provides a comprehensive tool for sustainable exploitation of natural resources

A global infrageneric classification system for the genus Crotalaria (Leguminosae) based on molecular and morphological evidence

Crotalaria is a large genus of 702 species with its centre of diversity in tropical Africa and Madagascar and secondary radiations in other parts of the world. The current infrageneric classification system is based on morphological and morphomet- ric studies of the African taxa only and is here re-evaluated using a phylogenetic approach. DNA sequences derived from the nuclear ITS and the plastid matK, psbA-trnH and rbcLa markers were analyzed using parsimony and model-based (Bayesian) approaches. The resultant molecular phylogeny allowed for a new interpretation of diagnostically important morphological characters, including specialisations of the calyx, keel, standard petal and style, which are variously convergent in several unrelated infrageneric groups. Of particular interest is the congruence between the new phylogeny and the distribution of stand- ard petal callosity types. A sectional classification system for the entire genus is proposed for the first time. The new system that is formalised here comprises eleven sections: Amphitrichae, Calycinae, Crotalaria, Geniculatae, Glaucae, Grandiflorae, Hedriocarpae, Incanae, Schizostigma, Borealigeniculatae and Stipulosae. Sectional limits of the Geniculatae, Calycinae and Crotalaria are modified. The subsections Stipulosae, Glaucae and Incanae are raised to sectional level, while some groups previously recognized as subsections are abandoned due to non-monophyly (subsections Chrysocalycinae, Hedriocarpae, Macrostachyae and Tetralobocalyx). Two new sections are recognized, Amphitrichae and Borealigeniculatae.Web of Scienc

Agile Multi-Scale Decompositions for Automatic Image Registration

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed

Angular versus radial correlation effects on momentum distributions of light two-electron ions

We investigate different correlation mechanisms for two-electron systems and compare their respective effects on various electron distributions. The simplicity of the wave functions used allows for the derivation of closed-form analytical expressions for all electron distributions. Among other features, it is shown that angular and radial correlation mechanisms have opposite effects on Compton profiles at small momenta.Comment: 22 pages, 5 figures, 3 tabl