Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A multiple Kernel learning algorithm for cell nucleus classification of renal cell carcinoma
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Evaluating feature combination in object classification
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Hybrid generative-discriminative nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Renal cancer cell classification using generative embeddings and information theoretic kernels
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.