203 research outputs found
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
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