Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat-map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.Comment: This version has been submitted to CAAI Transactions on Intelligence Technology. 202

A NOVEL IMPLEMENTATION ALGORITHM FOR CALCULATION OF

Common vector approach (CVA), discriminative common vector approach (DCVA) and linear regression classification (LRC) are subspace methods used in pattern recognition. Up to now, there were two well-known algorithms to calculate the common vectors: (i) by using the Gram-Schmidt orthogonalization process, (ii) by using the within-class covariance matrices. The purpose of this paper is to introduce a new implementation algorithm for the derivation of the common vectors using the linear regression idea. The derivation of the discriminative common vectors through LRC is also included in this paper. Two numerical examples are given to clarify the proposed derivations. An experimental work is given in AR face database to compare the recognition performances of CVA, DCVA, and LRC. Additionally, the three implementation algorithms of common vector are compared in terms of processing time efficiency

Two Dimensional (2D) Subspace Classifiers for Image Recognition

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Large margin classifiers based on affine hulls

Special Issue: 10th Brazilian Symposium on Neural Networks (SBRN2008)International audienceThis paper introduces a geometrically inspired large margin classifier that can be a better alternative to the support vector machines (SVMs) for the classification problems with limited number of training samples. In contrast to the SVM classifier, we approximate classes with affine hulls of their class samples rather than convex hulls. For any pair of classes approximated with affine hulls, we introduce two solutions to find the best separating hyperplane between them. In the first proposed formulation, we compute the closest points on the affine hulls of classes and connect these two points with a line segment. The optimal separating hyperplane between the two classes is chosen to be the hyperplane that is orthogonal to the line segment and bisects the line. The second formulation is derived by modifying the nu-SVM formulation. Both formulations are extended to the nonlinear case by using the kernel trick. Based on our findings, we also develop a geometric interpretation of the least squares SVM classifier and show that it is a special case of the proposed method. Multi-class classification problems are dealt with constructing and combining several binary classifiers as in SVM. The experiments on several databases show that the proposed methods work as good as the SVM classifier if not any better