J Regularization Improves Imbalanced Multiclass Segmentation

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. When adding a Youden's J statistic regularization term to the cross entropy loss we improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to correct results by helping advancing the optimization when cross entropy stagnates. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field images to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of images, some of which are poorly annotated

A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method

Heterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance

It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in anongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Application of data analytics for predictive maintenance in aerospace: an approach to imbalanced learning.

The use of aircraft operational logs to predict potential failure that may lead to disruption poses many challenges and has yet to be fully explored. These logs are captured during each flight and contain streamed data from various aircraft subsystems relating to status and warning indicators. They may, therefore, be regarded as complex multivariate time-series data. Given that aircraft are high-integrity assets, failures are extremely rare, and hence the distribution of relevant data containing prior indicators will be highly skewed to the normal (healthy) case. This will present a significant challenge in using data-driven techniques to 'learning' relationships/patterns that depict fault scenarios since the model will be biased to the heavily weighted no-fault outcomes. This thesis aims to develop a predictive model for aircraft component failure utilising data from the aircraft central maintenance system (ACMS). The initial objective is to determine the suitability of the ACMS data for predictive maintenance modelling. An exploratory analysis of the data revealed several inherent irregularities, including an extreme data imbalance problem, irregular patterns and trends, class overlapping, and small class disjunct, all of which are significant drawbacks for traditional machine learning algorithms, resulting in low-performance models. Four novel advanced imbalanced classification techniques are developed to handle the identified data irregularities. The first algorithm focuses on pattern extraction and uses bootstrapping to oversample the minority class; the second algorithm employs the balanced calibrated hybrid ensemble technique to overcome class overlapping and small class disjunct; the third algorithm uses a derived loss function and new network architecture to handle extremely imbalanced ratios in deep neural networks; and finally, a deep reinforcement learning approach for imbalanced classification problems in log- based datasets is developed. An ACMS dataset and its accompanying maintenance records were used to validate the proposed algorithms. The research's overall finding indicates that an advanced method for handling extremely imbalanced problems using the log-based ACMS datasets is viable for developing robust data-driven predictive maintenance models for aircraft component failure. When the four implementations were compared, deep reinforcement learning (DRL) strategies, specifically the proposed double deep State-action-reward-state-action with prioritised experience reply memory (DDSARSA+PER), outperformed other methods in terms of false-positive and false-negative rates for all the components considered. The validation result further suggests that the DDSARSA+PER model is capable of predicting around 90% of aircraft component replacements with a 0.005 false-negative rate in both A330 and A320 aircraft families studied in this researchPhD in Transport System