EGFR and KRAS mutation prediction on lung cancer through medical image processing and artificial intelligence

Lung cancer causes more deaths globally than any other type of cancer. To determine the best treatment, detecting EGFR and KRAS mutations is of interest. However, non-invasive ways to obtain this information are not available. In this study, an ensemble approach is applied to increase the performance of EGFR and KRAS mutation prediction from CT images using a small dataset. A new voting scheme, Selective Class Average Voting (SCAV) is proposed and its performance is assessed both for machine learning models and Convolutional Neural Networks (CNNs). For the EGFR mutation, in the machine learning approach, there was an increase in the Sensitivity from 0.66 to 0.75, and an increase in AUC from 0.68 to 0.70. With the deep learning approach an AUC of 0.846 was obtained with custom CNNs, and with SCAV the Accuracy of the model was increased from 0.80 to 0.857. Finally, when combining the best Custom and Pre-trained CNNs using SCAV an AUC of 0.914 was obtained. For the KRAS mutation both in the machine learning models (0.65 to 0.71 AUC) and the deep learning models (0.739 to 0.778 AUC) a significant increase in performance was found. This increase was even greater with Ensembles of Pre-trained CNNs (0.809 AUC). The results obtained in this work show how to effectively learn from small image datasets to predict EGFR and KRAS mutations, and that using ensembles with SCAV increases the performance of machine learning classifiers and CNNs.DoctoradoDoctor en Ingeniería de Sistemas y Computació

Random rotations in machine learning

This thesis discusses applications of random rotations in machine learning. Rotations of the feature space can lead to more diverse ensembles, better predictions, less complex classifiers and smoother decision boundaries. In Chapter 1 of this thesis, the feature space is randomly rotated and one or more independent base learner is constructed on each rotation. In the case of classification, each base learner receives one vote in the final ensemble prediction; for regressions, predictions are averaged. An empirical study demonstrates the efficacy of random rotations. Observing that not all rotations are equally effective, Chapter 2 is dedicated to the analysis of what makes a rotation effective and whether it is possible to emphasize such rotations in the final ensemble prediction. It is demonstrated that focusing on rotations that lead to simpler base learners leads to more compact ensembles and often increases predictive accuracy. In this chapter, predictions are aggregated in a parametric fashion, providing more weight to less complex predictors in the final ensemble. Multiple parametric forms are explored. Instead of constructing one or more predictor for each rotation, it is also possible to provide multiple rotations of the feature space to a single predictor. This effectively provides a single predictor with multiple simultaneous viewpoints on the same feature space. The first half of Chapter 3 explores this idea. A great benefit of this approach, when compared to the methods described in the earlier chapters, is that the aggregation of the predictions across multiple rotations becomes part of the training algorithm of the classifier, rather than being constructed exogenously. This also makes the approach viable for ensemble architectures with an interdependence between the base learners, such as boosting. Finally, an importance measure can be used not only to select the most salient features but also to determine the most helpful rotations. A different method of combining multiple rotations is to form a meta- or stacking predictor that leverages the base predictions on each rotation as inputs. This results in a generalization of the results of Chapter 2, whereby the aggregation becomes nonparametric in nature and local with respect to the decision boundary. In this context, extra care must be taken to avoid data snooping biases. A repeated, nested cross-validation technique is described in the second half of Chapter 3 to facilitate this process. The procedure directly answers the question if rotations are helpful for a specific data set and provides an avenue for selecting effective rotations. Chapter 4 is concerned with the impact random rotations have had on the scientific literature and open source software community since their introduction with the publication of our initial paper on the topic

Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation

In this paper, we provide an overview of the approach we used as team Gabibboost for the ACM RecSys Challenge 2023, organized by ShareChat and Moj. The challenge focused on predicting user activity in the online advertising setting based on impression data, in particular, predicting whether a user would install an advertised application using a high-dimensional anonymized feature vector. Our proposed solution is based on an ensemble model that combines the strengths of several machine learning sub-models, including CatBoost, LightGBM, HistGradientBoosting, and two hybrid models. Our proposal is able to harness the strengths of our models through a distribution shift postprocessing and fine-Tune the final prediction via a custom build pessimistic rescaling function. The final ensemble model allowed us to rank 1st on the academic leaderboard and 9th overall

Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning

Nowadays, weather prediction is based on numerical models of the physics of the atmosphere. These models are usually run multiple times based on randomly perturbed initial conditions. The resulting so-called ensemble forecasts represent distinct scenarios of the future and provide probabilistic projections. However, these forecasts are subject to systematic errors such as biases and they are often unable to quantify the forecast uncertainty adequately. Statistical postprocessing methods aim to exploit structure in past pairs of forecasts and observations to correct these errors when applied to future forecasts. In this thesis, we develop statistical postprocessing methods based on the central paradigm of probabilistic forecasting, that is, to maximize the sharpness subject to calibration. A wide range of statistical and machine learning methods is presented with a focus on novel neural network-based postprocessing techniques. In particular, we analyze the aggregation of distributional forecasts from neural network ensembles and develop statistical postprocessing methods for ensemble forecasts of wind gusts, with a focus on European winter storms

Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE

Online Structured Learning for Real-Time Computer Vision Gaming Applications

In recent years computer vision has played an increasingly important role in the development of computer games, and it now features as one of the core technologies for many gaming platforms. The work in this thesis addresses three problems in real-time computer vision, all of which are motivated by their potential application to computer games. We rst present an approach for real-time 2D tracking of arbitrary objects. In common with recent research in this area we incorporate online learning to provide an appearance model which is able to adapt to the target object and its surrounding background during tracking. However, our approach moves beyond the standard framework of tracking using binary classication and instead integrates tracking and learning in a more principled way through the use of structured learning. As well as providing a more powerful framework for adaptive visual object tracking, our approach also outperforms state-of-the-art tracking algorithms on standard datasets. Next we consider the task of keypoint-based object tracking. We take the traditional pipeline of matching keypoints followed by geometric verication and show how this can be embedded into a structured learning framework in order to provide principled adaptivity to a given environment. We also propose an approximation method allowing us to take advantage of recently developed binary image descriptors, meaning our approach is suitable for real-time application even on low-powered portable devices. Experimentally, we clearly see the benet that online adaptation using structured learning can bring to this problem. Finally, we present an approach for approximately recovering the dense 3D structure of a scene which has been mapped by a simultaneous localisation and mapping system. Our approach is guided by the constraints of the low-powered portable hardware we are targeting, and we develop a system which coarsely models the scene using a small number of planes. To achieve this, we frame the task as a structured prediction problem and introduce online learning into our approach to provide adaptivity to a given scene. This allows us to use relatively simple multi-view information coupled with online learning of appearance to efficiently produce coarse reconstructions of a scene

Stress, Strain, or Energy: Which One Is the Superior Parameter to Estimate Fatigue Life of Notched Components? An Answer by a Novel Machine Learning-Based Framework

This paper introduces a simple framework for accurately predicting the fatigue lifetime of notched components by employing various machine learning algorithms applied to a wide range of materials, loading conditions, notch geometries, and fatigue lives. Traditional approaches for this task have relied on empirical relationships involving one of the mechanical properties, such as stress, strain, or energy. This study goes further by exploring which mechanical property serves as a better measure. The key idea of the framework is to use the gradient of the mechanical properties (stress, strain, and energy) to distinguish between different notch geometries. To demonstrate the accuracy and broad applicability of the framework, it is initially validated using isotropic materials, subsequently applied to samples produced through additive manufacturing techniques, and ultimately tested on carbon fiber laminated composites. The research demonstrates that the gradient of all three measures can be effectively employed to estimate fatigue lifetime, with stress-based predictions exhibiting the highest accuracy. Among the machine learning algorithms investigated, Gradient Boosting and Random Forest yield the most successful results. A noteworthy finding is the significant improvement in prediction accuracy achieved by incorporating new data generated based on the Basquin equation