Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patch...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WSI is disease-positive area. However, WSI representation learning still remains an open problem due to: (1) patch sampling on a higher resolution may be incapable of depicting microenvironment information such as the relative position between the tumor cells and surrounding tissues, while patches at lower resolution lose the fine-grained detail;(2) extracting patches from giant WSI results in large bag size, which tremendously increases the computational cost. To solve the problems, this paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Precisely, we randomly extract instant-level patch features from WSIs with different magnification. then a co-attention mapping between imaging and genomics is learned to uncover the pairwise interaction and reduce the space complexity of imaging features. Such early fusion makes it computationally feasible to use MIL Transformer for the survival prediction task. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability. We evaluate our approach on five cancer types from the Cancer Genome Atlas database and achieved an average c-index of 0.673, outperforming the state-of-the-art multimodality methods.
A class-modular classifier can be characterized by two prominent features: low classifier complexity and independence of classes. While conventional character recognition systems adopting the class modularity are fait...
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While partial discharge (PD) patternrecognition is widely considered as an important method to test insulation degradation, the major problem encountered in recognition is to extract PD pattern features. According to...
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ISBN:
(纸本)0780354591;0780354605
While partial discharge (PD) patternrecognition is widely considered as an important method to test insulation degradation, the major problem encountered in recognition is to extract PD pattern features. According to the analysis to three-dimensional statistical spectrum of PD, this paper adopted fractal theory to extract PD pattern features. From designed model tests and fraction calculation results, it is shown the method in this paper is effective to extract PD pattern features which are helpful in PD patternrecognition on the base of the three-dimensional statistical spectrum.
the proceedings contain 238 papers. the topics discussed include: a candidate reduction method for handwritten kanji character recognition;recognition of unconstrained handwritten numeral strings using decision value ...
ISBN:
(纸本)0769512631
the proceedings contain 238 papers. the topics discussed include: a candidate reduction method for handwritten kanji character recognition;recognition of unconstrained handwritten numeral strings using decision value generator;a scanning n-tuple classifier for online recognition of handwritten digits;handwriting recognition using local methods for normalization and global methods for recognition;handwritten character recognition using piecewise linear two-dimensional warping;separation of overlapping text from graphics;binarization of document images using image dependent model;character spotting using image-based stochastic models;a class-modularity for character recognition;planar Markov modeling for Arabic writing recognition: advancement state;and character pre-classification based on fuzzy typographical analysis.
We set out an object localization scheme based on a convex programming matching method. the proposed approach is designed to match general objects, especially objects with very little texture, and in strong background...
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ISBN:
(纸本)0769525210
We set out an object localization scheme based on a convex programming matching method. the proposed approach is designed to match general objects, especially objects with very little texture, and in strong background clutter;traditional methods have great difficulty in such situations. We propose a convex quadratic programming (CQP) relaxation method to solve the problem more robustly. the CQP relaxation uses a small number of basis points to represent the target point space and therefore can be used in very large scale matching problems. We further propose a successive convexification scheme to improve the matching accuracy. Scale and rotation estimation is integrated as well so that the proposed scheme can be applied to general conditions. Experiments show very promising results for the proposed method in object localization applications.
the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a d...
ISBN:
(纸本)9783540225706
the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a demonstration on text data;the use of graph techniques for identifying objects and scenes in indoor building environments for mobile robots;graphical-based learning environments for patternrecognition;spectral analysis of complex laplacian matrices;a significant improvement of softassign with diffusion kernels;eigenspace method by autoassociative networks for object recognition;extraction of skeletal shape features using a visual attention operator;computing the cyclic edit distance for pattern classification by ranking edit paths;steady state random walks for path estimation;new variational framework for rigid-body alignment;an error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification;comparison of algorithms for web document clustering using graph representations of data;a syntactic patternrecognition approach to computer assisted translation;a general methodology for finite-state translation using alignments;a comparison of unsupervised shot classification algorithms for news video segmentation;diagnosis of lung nodule using the semivariogram function;distances between distributions;multiscale curvature assessment of postural deviations;learning people movement model from multiple cameras for behaviour recognition;a comparison of least squares and spectral methods for attributed graph matching and an auction algorithm for graph-based contextual correspondence matching.
Texture can be interpreted as a measure of the edginess about a pixel and can be described by edge co-occurrence matrices. When the matrix is decomposed using discrete 2-dimensional orthogonal Hermite functions, the c...
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In this paper we show how inexact multisubgraph matching can be solved using methods based on the projections of vertices (and their connections) into the eigenspaces of graphs - and associated clustering methods. Our...
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