Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients

Introduction Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer (LARC). Radiomics can be used as noninvasive biomarker for prediction of response to therapy. The main aim of this study was to evaluate the association of MRI texture features of LARC with nCRT response and the effect of Laplacian of Gaussian (LoG) filter and feature selection algorithm in prediction process improvement. Methods All patients underwent MRI with a 3T clinical scanner, 1 week before nCRT. For each patient, intensity, shape, and texture-based features were derived from MRI images with LoG filter using the IBEX software and without preprocessing. We identified responder from a non-responder group using 9 machine learning classifiers. Then, the effect of preprocessing LoG filters with 0.5, 1 and 1.5 value on these classification algorithms' performance was investigated. Eventually, classification algorithm's results were compared in different feature selection methods. Result Sixty-seven patients with LARC were included in the study. Patients' nCRT responses included 11 patients with Grade 0, 19 with Grade 1, 26 with Grade 2, and 11 with Grade 3 according to AJCC/CAP pathologic grading. In MR Images which were not preprocessed, the best performance was for Ada boost classifier (AUC = 74.8) with T2W MR Images. In T1W MR Images, the best performance was for aba boost classifier (AUC = 78.1) with a sigma = 1 preprocessing LoG filter. In T2W MR Images, the best performance was for naive Bayesian network classifier (AUC = 85.1) with a sigma = 0.5 preprocessing LoG filter. Also, performance of machine learning models with CfsSubsetEval (CF SUB E) feature selection algorithm was better than others. Conclusion Machine learning can be used as a response predictor model in LARC patients, but its performance should be improved. A preprocessing LoG filter can improve the machine learning methods performance and at the end, the effect of feature selection algorithm on model's performance is clear. Keywords:MRI; Rectal cancer; Radiomics; Machine learnin

Transfer Learning for Hyperspectral Images Utilizing Channel Selection Techniques and Ensemble Methods

Hyperspectral images contain information from a wider range of the electromagnetic spectrum than natural images which gives them potential for better classification ability. However, hyperspectral datasets are typically small due to the expensive equipment needed to obtain the images, which can limit classification performance. One solution to this problem is transfer learning, in which a model trained on one dataset is reused for a separate dataset. Research has shown that transfer learning between hyperspectral datasets can give improved performance over models without transfer learning when training data are limited. Since extra hyperspectral data are not always available, the solution proposed here is to instead use networks pretrained on natural image (i.e., red, blue, green, or RGB) datasets for transfer learning. By using various feature selection and feature extraction methods, extracted hyperspectral samples are transformed into a three-channel format to imitate an RGB image and are used for fine tuning the well-known ResNet, DenseNet, and VGG networks. Feature extraction methods include techniques like principal component analysis, which create lower dimensional features from high dimensional spectral data. Alternatively, feature selection methods aim to find the best set of existing channels to use for classification. Experimental results are obtained using two well-known hyperspectral datasets, showing 73.6% accuracy on Pavia University and 82.8% accuracy on Salinas with 25 training samples per class. Additional ensemble methods are implemented that utilize multiple networks and show an increase in accuracy of 4.4% and 3% for Pavia University and Salinas, respectively. These results demonstrate that networks pretrained on RGB datasets are suitable for transfer learning with hyperspectral image datasets and can achieve desirable performance given the proper preprocessing technique

Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection

AbstractFor a hybrid medical image retrieval system, a genetic algorithm (GA) approach is presented for the selection of dimensionality reduced set of features. This system was developed in three phases. In first phase, three distinct algorithm are used to extract the vital features from the images. The algorithm devised for the extraction of the features are Texton based contour gradient extraction algorithm, Intrinsic pattern extraction algorithm and modified shift invariant feature transformation algorithm. In the second phase to identify the potential feature vector GA based feature selection is done, using a hybrid approach of “Branch and Bound Algorithm” and “Artificial Bee Colony Algorithm” using the breast cancer, Brain tumour and thyroid images. The Chi Square distance measurement is used to assess the similarity between query images and database images. A fitness function with respect Minimum description length principle were used as initial requirement for genetic algorithm. In the third phase to improve the performance of the hybrid content based medical image retrieval system diverse density based relevance feedback method is used. The term hybrid is used as this system can be used to retrieve any kind of medical image such as breast cancer, brain tumour, lung cancer, thyroid cancer and so on. This machine learning based feature selection method is used to reduce the existing system dimensionality problem. The experimental result shows that the GA driven image retrieval system selects optimal subset of feature to identify the right set of images

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction

Research related to Biology often utilizes machine learning models that are ultimately uninterpretable by the researcher. It would be helpful if researchers could leverage the same computing power but instead gain specific insight into decision-making to gain a deeper understanding of their domain knowledge. This paper seeks to select features and derive rules from a machine learning classification problem in biochemistry. The specific point of interest is five species of Glycyrrhiza, or Licorice, and the ability to classify them using High-Performance Thin Layer Chromatography (HPTLC) images. These images were taken using HPTLC methods under varying conditions to provide eight unique views of each species. Each view contains 24 samples with varying counts of the individual species. There are a few techniques applied for feature selection and rule extraction. The first two are based on methods recently pioneered and presented as “Binary Encoding of Random Forests” and “Rule Extraction using Sparse Encoding” (Liu 2012). In addition, an independently developed technique called “Interval Extraction and Consolidation” was applied, which was conceptualized due to the particular nature of the dataset. Altogether, these techniques used in consort with standard machine learning models could narrow a feature space from around one-thousand candidates to only ten. These ten most critical features were then used to derive a set of rules for the classification of the five species of licorice. Regarding feature selection, compared to standard model parameter optimization, the Binary Encoding of Random Forests performed similarly, if not much better, in reducing the feature space in almost all cases. Additionally, the application of Interval Extraction and Consolidation excelled in further simplifying the reduced feature space, often by another factor of five to ten. The selected features were then used for relatively simple rule extraction using decision trees, allowing for a more interpretable model