Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011; 14th International C Gabor Fichtinger,Anne Martel,Terry Peters Co

物质

adduction

演讲

Regularized Tensor Factorization for Multi-Modality Medical Image Classification. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework base

Substance

arbovirus

Aggregated Distance Metric Learning (ADM) for Image Classification in Presence of Limited Training D this approach is effective in the presence of large amounts of training data, classification accuracy will deteriorate when the number of training samples is small, which, unfortunately, is often the situation in several medical applications. We present a novel image classification method called .(

Perigee

RAG

A Discriminative-Generative Model for Detecting Intravenous Contrast in CT Imagesnreliability of the existing DICOM contrast metadata..The algorithm is based on a hybrid discriminative-generative probabilistic model. A discriminative detector localizes enhancing regions of interest in the scan. Then a generative classifier optimally fuses evidence gathered from those regions int

elastic

expdient

An Analysis of Scale and Rotation Invariance in the Bag-of-Features Method for Histopathological Imay the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled

荨麻

Robust Large Scale Prone-Supine Polyp Matching Using Local Features: A Metric Learning Approachpolyps. In this paper, we propose a robust and automatic polyp prone-supine view matching method, to facilitate the regular CTC workflow where radiologists need to manually match the CAD findings in prone and supine CT scans for validation. Apart from previous colon registration approaches based on