다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

/pdf/a-density-based-algorithm-for-discovering-clusters-in-large-1o5u8sf38m.pdf

A density-based algorithm for discovering clusters in large spatial Databases with Noise

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From July 1982 to August 1994, CEA levels were determined in 298 mammary tissue samples (30 benign, 242 primary breast cancer and 26 metastatic breast cancer) CEA serum levels were also evaluated in 30 patients with benign diseases, 153 patients with primary breast cancer and 26 patients with metastates CEA tissue levels in both pellet and cytosol were significantly higher in samples from cancerous than from non malignant tissues (p<00001), and higher in the pellet than in the cytosol (p<00001) CEA in the pellet and cytosol were related to steroid receptors, with the highest levels being observed in ER+/PR+ tumors (p<0001) They were, however, not related to other pathological parameters such as tumor size or nodes There was a correlation between CEA pellet and CEA serum in both patients with primary or metastatic tumors, with significantly higher CEA serum levels in patients with CEA pellet positivity than in those with CEA pellet negativity CEA serum levels were a prognostic factor (DFS and OS) in the whole group as well as in node-positive and node- negative breast cancer patients This prognostic value was only found in patients with CEA pellet positivity In the follow-up of 143 NED patients, abnormal CEA serum levels rose prior to the diagnosis of relapse in 73% (29/40)of CEA pellet+ patients with distant recurrences but in only 9% (2/22) of CEA pellet- cases (p<00001) In , CEA evaluation in tissue improves the CEA clinical application, selecting those patients whose serum CEA will be useful in the prognosis and early diagnosis of recurrence

Carcinoembryonic antigen in tissue and serum from breast cancer patients relationship with steroid receptors and clinical applications in the prognosis and early diagnosis of relapse.

A variational approach for sparse component estimation and low-rank matrix recovery

Multispectral image reconstruction allows to combine a multispectral low resolution image with a panchromatic high resolution one to obtain a new multispectral image with the spectral properties of the lower resolution image and the level of detail of the higher resolution one. In this paper we formulate the problem of multispectral image reconstruction based on super-resolution techniques and derive an iterative method to estimate the high resolution multispectral image from the observed images. Finally, the proposed method is tested on a real Landsat 7 ETM+ image.

Super resolution reconstruction of multispectral images

In this paper we consider the problem of reconstructing a high resolution image from a set of undersampled and degraded frames, all of them obtained from high resolution images with unknown shifting displacements between them. We derive an iterative method to estimate the unknown shifts and the high resolution image given the low resolution observations. Finally, the proposed method is tested on real images.

Estimation of high resolution images and registration parameters from low resolution observations

In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. A sparse image prior based on the horizontal and vertical first order differences is combined with a non-sparse SAR prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational posterior distribution approximation on each posterior will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimize a linear convex combination of the Kullback-Leibler (KL) divergences associated with each posterior distribution. We find this distribution in closed form. The estimated HR images are compared with images provided by other SR reconstruction methods.

/pdf/image-prior-combination-in-super-resolution-image-2670tu45wh.pdf

Rafael Molina

Papers

Carcinoembryonic antigen in tissue and serum from breast cancer patients relationship with steroid receptors and clinical applications in the prognosis and early diagnosis of relapse.

A variational approach for sparse component estimation and low-rank matrix recovery

Super resolution reconstruction of multispectral images

Estimation of high resolution images and registration parameters from low resolution observations

Image prior combination in super-resolution image registration & reconstruction