Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Regression Concept Vectors for Bidirectional Explanations in Histopathology

Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making. In this work, we propose a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer. The directional derivative of the decision function along the RCVs represents the network sensitivity to increasing values of a given concept measure. When applied to breast cancer grading, nuclei texture emerges as a relevant concept in the detection of tumor tissue in breast lymph node samples. We evaluate score robustness and consistency by statistical analysis.Comment: 9 pages, 3 figures, 3 table

Origin of ferromagnetism in (Zn,Co)O from magnetization and spin-dependent magnetoresistance

In order to elucidate the nature of ferromagnetic signatures observed in (Zn,Co)O we have examined experimentally and theoretically magnetic properties and spin-dependent quantum localization effects that control low-temperature magnetoresistance. Our findings, together with a through structural characterization, substantiate the model assigning spontaneous magnetization of (Zn,Co)O to uncompensated spins at the surface of antiferromagnetic nanocrystal of Co-rich wurtzite (Zn,Co)O. The model explains a large anisotropy observed in both magnetization and magnetoresistance in terms of spin hamiltonian of Co ions in the crystal field of the wurtzite lattice.Comment: 6 pages, 6 figure

Visualizing and Interpreting Feature Reuse of Pretrained CNNs for Histopathology

Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel activations of deep layers in the pretrained network are not present after finetuning. The learned features seem to be used by the network to spot atypical nuclei in the images, as shown by class activation maps. Finally, texture measures appear discriminative after finetuning, as shown by accurate Regression Concept Vectors

Spin-related magnetoresistance of n-type ZnO:Al and Zn_{1-x}Mn_{x}O:Al thin films

Effects of spin-orbit coupling and s-d exchange interaction are probed by magnetoresistance measurements carried out down to 50 mK on ZnO and Zn_{1-x}Mn_{x}O with x = 3 and 7%. The films were obtained by laser ablation and doped with Al to electron concentration ~10^{20} cm^{-3}. A quantitative description of the data for ZnO:Al in terms of weak-localization theory makes it possible to determine the coupling constant \lambda_{so} = (4.4 +- 0.4)*10^{-11} eVcm of the kp hamiltonian for the wurzite structure, H_{so} = \lambda_{so}*c(s x k). A complex and large magnetoresistance of Zn_{1-x}Mn_{x}O:Al is interpreted in terms of the influence of the s-d spin-splitting and magnetic polaron formation on the disorder-modified electron-electron interactions. It is suggested that the proposed model explains the origin of magnetoresistance observed recently in many magnetic oxide systems.Comment: 4 pages, 4 figure