Unlearning Spurious Correlations in Chest X-ray Classification

Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achieving model generalization, it is important to acknowledge that the diverse nature of these sources inherently introduces unintended confounders and other challenges that can impact both model accuracy and transparency. A notable confounding factor in medical image classification, particularly in musculoskeletal image classification, is skeletal maturation-induced bone growth observed during adolescence. We train a deep learning model using a Covid-19 chest X-ray dataset and we showcase how this dataset can lead to spurious correlations due to unintended confounding regions. eXplanation Based Learning (XBL) is a deep learning approach that goes beyond interpretability by utilizing model explanations to interactively unlearn spurious correlations. This is achieved by integrating interactive user feedback, specifically feature annotations. In our study, we employed two non-demanding manual feedback mechanisms to implement an XBL-based approach for effectively eliminating these spurious correlations. Our results underscore the promising potential of XBL in constructing robust models even in the presence of confounding factors.Comment: Accepted at the Discovery Science 2023 conference. arXiv admin note: text overlap with arXiv:2307.0602

FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose 'Few Shot anOMaly detection' (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on 'few' examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, 'Stop Loss', improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at state-of-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://github.com/niamhbelton/FewSOME

Effect Of Emulsification And Blending On The Oxygenation And Substitution Of Diesel Fuel For Compression Ignition Engine

Global emission standards are getting more stringent in which the existing diesel engine technologies are on the brink of losing their permit to operate. While there are successful engine side researches that can target the current emission norms, their implementation in existing engines will not be possible due to their higher price tag. With this respect, fuel side improvement with no or minimal modification to engine hardware is the best way to address the issue in the existing engines. The commonly used fuel oxygenators in diesel engines are water, alcohol, biodiesel and the combinations of these. The method of oxygenation and their corresponding results on the combustion, performance and emissions that have been reported in the literatures are widely varied. The current review article targets the blending and emulsification techniques used in the oxygenation and fuel substitution of diesel. Based on the misconceptions about the stability of emulsions, many researchers are found to use the term blending even though the technique they have used is emulsification. While blending of fuels is convenient for fuels which have relatively similar boiling temperature, emulsification technique should be followed for fuel mixtures of varied boiling temperature so that the benefit of micro-explosion can be reflected in the fuel atomization. Secondary atomization resulting from the micro-explosion phenomenon of emulsified fuels and fuel oxygenation are responsible for the improvement of combustion, performance and CO and PM emissions. Latent heat of vaporization is found to be responsible for the reduction of NOx emissions

Distance-Aware eXplanation Based Learning

eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL augments loss functions to penalize a model based on deviation of its explanations from user annotation of image features. The literature on XBL mostly depends on the intersection of visual model explanations and image feature annotations. We present a method to add a distance-aware explanation loss to categorical losses that trains a learner to focus on important regions of a training dataset. Distance is an appropriate approach for calculating explanation loss since visual model explanations such as Gradient-weighted Class Activation Mapping (Grad-CAMs) are not strictly bounded as annotations and their intersections may not provide complete information on the deviation of a model's focus from relevant image regions. In addition to assessing our model using existing metrics, we propose an interpretability metric for evaluating visual feature-attribution based model explanations that is more informative of the model's performance than existing metrics. We demonstrate performance of our proposed method on three image classification tasks.Comment: Accepted at the 35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 202

Correlation between caffeine contents of green coffee beans and altitudes of the coffee plants grown in southwest Ethiopia

Caffeine contents of 45 green coffee bean samples collected from coffee plants grown at different altitudes in Southwest Ethiopia was determined by UV-Vis spectrophotometry. The caffeine contents were found in the range of 0.62 - 1.2% (w/w). A moderate negative correlation (R = 0.5463) was found between the caffeine contents of green coffee beans and the altitudes at which the coffee plants were grown. The caffeine contents of 9 of the green coffee bean samples analyzed by high performance liquid chromatography (HPLC) provided comparable results in the range of 0.60−1.1% (w/w). Statistical analysis of data (t-test) indicated absence of significant differences between the caffeine contents obtained by the two methods. Nonetheless, HPLC method is precise, accurate and reliable in determining caffeine content in green coffee bean samples while the UV-Vis spectrophotometry is simple, rapid, precise and more economical. KEY WORDS: Green coffee beans, Caffeine, Correlation between caffeine content and altitude of coffee plant, UV-Vis spectrophotometry, High performance liquid chromatography, Ethiopia Bull. Chem. Soc. Ethiop. 2018, 32(1), 13-25DOI: https://dx.doi.org/10.4314/bcse.v32i1.

Predictors of hospitalization among children on ART in Ethiopia: A cohort study

Background: Substantial progress has been made in the management of pediatric HIV infection in Ethiopia with the implementation of mother-to-child-prevention programs. Since the introduction of HAART in 2005, mortality among HIV-infected children has reduced while the rate of hospitalization was expected to rise. The purpose of this study, therefore, was to assess predictors of hospitalization in children on ART in seven university referral hospitals in Ethiopia.Methods: A prospective cohort study design was employed on children age 0-18 years as part of a multisite observational study. ART-experienced eligible and  ART-naïve children with HIV/AIDS were enrolled into the Advanced Clinical  Monitoring (ACM) till December 31, 2012 were included. From the database,  information on hospitalization and other independent variables were extracted. Analysis was done using both SPSS for Windows version 16.0 and STATA.  Descriptive analyses and modeling was done using logistic regression.Results: Of the 405 children on ART (174 experienced, 231 naive), 86 (20.7%)  were hospitalized for various reasons; two children were excluded since they were hospitalized for unrelated conditions (appendicitis and burn). Fifty one (60.7%) of  the eighty four admitted children were hospitalized in the first six months of ART  initiation. Of the independent variables, only the presence of opportunistic infections and duration on ART were significantly associated with hospitalization both on bi-variable and multivariable analyses (P-value <0.05). As the duration on ART increased by one month, the risk of hospitalization decreased by 5.4%, which is statistically significant (P < 0.001). Whereas the incidence (number) of OI’s increased by one, the risk of being hospitalized increased by 35.2% (P = 0.002). Of the individual opportunistic infections, pneumonia was found to be the only predictor of hospitalization (P-value = 0.002).Conclusion: This study showed that nearly two-third of the hospitalization was within 6 months of initiation of ART; and presence of OI and duration on ART were the only predictors of hospitalization.Key words: Hospitalization, Children, HIV/AIDS, HAAR