Bagging Tree Classifier and Texture Features for Tumor Identification in Histological Images

The goal of this challenge 1 was to evaluate new and existing algorithms for automated detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images of lymph node sections. To this end, both slide-based and lesion-based evaluation was made. Several classification methods were...

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Bibliographic Details
Main Authors: Fernández-Carrobles, M. Milagro, Serrano, Ismael, Bueno, Gloria, Déniz, Oscar
Format: Conference Proceeding
Language:English
Subjects:
automatic classification
texture features
Whole-slide images
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Summary:The goal of this challenge 1 was to evaluate new and existing algorithms for automated detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images of lymph node sections. To this end, both slide-based and lesion-based evaluation was made. Several classification methods were tested including classical and deep learning techniques. The most efficient one for the dataset tested was Bagging Tree classifiers using texture features. In the slide-based classification an AUC equal to 0.9952 was obtained, with 98.13% of TP and 1.28% of FP. The TP result decreases in the lesion-based evaluation. Two methodologies were proposed for this second evaluation. Method 1 was based on the convex area of the regions and method 2 based on morphophone- mic, geometric and statistical features. The sensitivity for lesion detection was 39.83% and 36.66% respectively, though the false positive average is kept low about 12.28 in method 1 and 10.71 in method 2.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.07.030