7,699 research outputs found
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
10001000 (0.5mm0.5mm). Training data can be generated according
to preferences of algorithm designer and can range from approximately 27,000 to
over 50,000 patches if the preset parameters are adopted. We propose a compound
patch-and-scan accuracy measurement that makes achieving high accuracies quite
challenging. In addition, we set the benchmarking line by applying LBP,
dictionary approach and convolutional neural nets (CNNs) and report their
results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for
Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to
improve example-based single image super resolution (SR): 1) augmentation of
data, 2) use of large dictionaries with efficient search structures, 3)
cascading, 4) image self-similarities, 5) back projection refinement, 6)
enhanced prediction by consistency check, and 7) context reasoning. We validate
our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and
methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial
improvements.The techniques are widely applicable and require no changes or
only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method
sets new state-of-the-art results outperforming A+ by up to 0.9dB on average
PSNR whilst maintaining a low time complexity.Comment: 9 page
A What-and-Where Neural Network for Invariant Image Preprocessing
A feedforward neural network for invariant image preprocessing is proposed that represents the position1 orientation and size of an image figure (where it is) in a multiplexed spatial map. This map is used to generate an invariant representation of the figure that is insensitive to position1 orientation, and size for purposes of pattern recognition (what it is). A multiscale array of oriented filters followed by competition between orientations and scales is used to define the Where filter.British Petroleum (89-A-1024); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N0014-91-J-4100); Air Force Office of Scientific Research (90-0175); NSF Graduate Fellowshi
Fast fluorescence microscopy for imaging the dynamics of embryonic development
Live imaging has gained a pivotal role in developmental biology since it increasingly allows real-time observation of cell behavior in intact organisms. Microscopes that can capture the dynamics of ever-faster biological events, fluorescent markers optimal for in vivo imaging, and, finally, adapted reconstruction and analysis programs to complete data flow all contribute to this success. Focusing on temporal resolution, we discuss how fast imaging can be achieved with minimal prejudice to spatial resolution, photon count, or to reliably and automatically analyze images. In particular, we show how integrated approaches to imaging that combine bright fluorescent probes, fast microscopes, and custom post-processing techniques can address the kinetics of biological systems at multiple scales. Finally, we discuss remaining challenges and opportunities for further advances in this field
Keeping track of worm trackers
C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Guiding Eye Movements For Feature Based Shape Matching
We introduce a novel method for shape-based image database search that uses saccadic targeting for local feature choice. A simulated multiresolution sensor is directed toward salient regions of an image in a series of saccadic movements. At each fixation point 1 a region of the retinal image is stored for later matching by correlation. The utility of this approach is demonstrated on an 86 image database
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