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

Applications of Machine Learning Algorithms in Nitrogen Fertilizer Management of Triticale

1055-1063In this study, a new classification technique is proposed to distinguish the appropriate one from four different nitrogen (N)fertilizer doses (0, 40, 80, and 160 kg ha−1) using six triticale cultivars. In the classification phase, nine yield featuresfrom 30 plants of the same cultivar were measured, that is, each dose or class has 30 feature vectors consisting of ninefeatures. Next, six triticale cultivars were classified for each dose of N fertilizer separately by using 30 feature vectorsbelonging to each dose. Similarly, the same classification task was repeated by using all feature vectors taken from fourdoses of N fertilizer. What makes this study novel is the classification process of six triticale cultivars by taking into accounttheir characters based on different doses of N fertilizer. The classification tasks were conducted by applying CommonVector Approach, Support Vector Machine, k-Nearest Neighbor, and Decision Trees algorithms. While satisfactory resultswere obtained from the training sets for all cases, the test set accuracy is relatively lower for the classification of four dosesof N fertilizer and six cultivars since features extracted from different doses of N fertilizer for the same cultivar are close toeach other. Furthermore, the number of feature vectors is insufficient to classify classes efficiently. Interestingly, when thecommon information of the classifiers was extracted with the biplot technique, useful results were obtained in selectingappropriate N doses for several triticale varieties. Combined with the results of future comprehensive studies, applicableresults for the agricultural sector can be proposed