arXiv subjects

Kevin Dick

Publications and source records attributed to Kevin Dick.

7 recordsLinked to original sources

arXiv2026

ExaTune: Exhaustive Hyperparameter Landscape Exploration at Scale

Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explored performance landscape that encodes how results depend on configuration choices. Fitness landscape analysis comprises a robust collection of methods from evolutionary computation offering ruggedness indices, autocorrelation functions, and fitness-distance correlation that have never been applied to machine learning hyperparameter spaces at scale, given that fine-grained ground-truth enumeration data has not existed. We present ExaTune, an open-source HPC system to produce a new artifact class: the hyperparameter landscape map. ExaTune exhaustively evaluates every point on a discretized hyperparameter grid via $K$-fold cross-validation, aggregates results into reproducible parquet benchmarks, and applies a suite of five formalized landscape metrics: spatial autocorrelation (Moran's $I$), ruggedness, local optima density, fitness-distance correlation (FDC), and variance-based parameter importance. We release a benchmark collection of 8 complete landscape maps (totalling 285,080 exhaustively evaluated configurations) spanning five model families (SVMs, random forests, XGBoost, ridge regression, and $k$-nearest neighbors) across six classification and two regression benchmarks, hosted on HuggingFace. Empirical analysis demonstrates that Bayesian optimization, Optuna, Hyperband, and Random Search at standard trial budgets systematically miss instability regions, secondary optima, and parameter interaction effects that are visible in complete landscapes; information that is structurally unavailable to any optimizer by design.

cs.LG↗

arXiv2025

Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging

Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the performance of a self-supervised ultrasound foundation model for automated fetal renal anomaly classification using a curated dataset of 969 two-dimensional ultrasound images. A pretrained Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE) was fine-tuned for binary and multi-class classification of normal kidneys, urinary tract dilation, and multicystic dysplastic kidney. Models were compared with a DenseNet-169 convolutional baseline using cross-validation and an independent test set. USF-MAE consistently improved upon the baseline across all evaluation metrics in both binary and multi-class settings. USF-MAE achieved an improvement of about 1.87% (AUC) and 7.8% (F1-score) on the validation set, 2.32% (AUC) and 4.33% (F1-score) on the independent holdout test set. The largest gains were observed in the multi-class setting, where the improvement in AUC was 16.28% and 46.15% in F1-score. To facilitate model interpretability, Score-CAM visualizations were adapted for a transformer architecture and show that model predictions were informed by known, clinically relevant renal structures, including the renal pelvis in urinary tract dilation and cystic regions in multicystic dysplastic kidney. These results show that ultrasound-specific self-supervised learning can generate a useful representation as a foundation for downstream diagnostic tasks. The proposed framework offers a robust, interpretable approach to support the prenatal detection of renal anomalies and demonstrates the promise of foundation models in obstetric imaging.

eess.IV↗

arXiv2025

Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly

The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated dataset of fetal brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.

eess.IV↗

arXiv2023

CAManim: Animating end-to-end network activation maps

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions.

cs.CV↗

arXiv2023

MetaCAM: Ensemble-Based Class Activation Map

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends largely on experimental parameters such as the selected image, target class, and model. Here, we propose MetaCAM, an ensemble-based method for combining multiple existing CAM methods based on the consensus of the top-k% most highly activated pixels across component CAMs. We perform experiments to quantifiably determine the optimal combination of 11 CAMs for a given MetaCAM experiment. A new method denoted Cumulative Residual Effect (CRE) is proposed to summarize large-scale ensemble-based experiments. We also present adaptive thresholding and demonstrate how it can be applied to individual CAMs to improve their performance, measured using pixel perturbation method Remove and Debias (ROAD). Lastly, we show that MetaCAM outperforms existing CAMs and refines the most salient regions of images used for model predictions. In a specific example, MetaCAM improved ROAD performance to 0.393 compared to 11 individual CAMs with ranges from -0.101-0.172, demonstrating the importance of combining CAMs through an ensembling method and adaptive thresholding.

cs.CV↗

arXiv2022

Generalized Reciprocal Perspective

Across many domains, real-world problems can be represented as a network. Nodes represent domain-specific elements and edges capture the relationship between elements. Leveraging high-performance computing and optimized link prediction algorithms, it is increasingly possible to evaluate every possible combination of nodal pairs enabling the generation of a comprehensive prediction matrix (CPM) that places an individual link prediction score in the context of all possible links involving either node (providing data-driven context). Historically, this contextual information has been ignored given exponentially growing problem sizes resulting in computational intractability; however, we demonstrate that expending high-performance compute resources to generate CPMs is a worthwhile investment given the improvement in predictive performance. In this work, we generalize for all pairwise link-prediction tasks our novel semi-supervised machine learning method, denoted Reciprocal Perspective (RP). We demonstrate that RP significantly improves link prediction accuracy by leveraging the wealth of information in a CPM. Context-based features are extracted from the CPM for use in a stacked classifier and we demonstrate that the application of RP in a cascade almost always results in significantly (p < 0.05) improved predictions. These results on RS-type problems suggest that RP is applicable to a broad range of link prediction problems.

cs.LG↗

arXiv2016

Identifying Unvaccinated Individuals in Canada: A Predictive Model

Recently, the media and public health officials have become increasingly aware of the rise in anti-vaccine sentiment. Vaccinations have numerous health benefits for immunized individuals as well as for the general public through herd immunity. Given the rise in immunization-preventable diseases, a consequence of people opting out of their routine vaccinations, we determined that Canadian health data can identify individuals over the age of 60 who chose not to get vaccinated (80.1% negative predictive value) and individuals under the age of 60 who have recently been vaccinated (96.4% positive predictive value). Using the 2009-2014 Canadian Community Health Surveys (CCHS), a probit model identified the variables that were most commonly associated with flu vaccination outcomes. Of 1,381 variables, 47 with the most significant marginal effects were selected, including the presence of diseases (e.g. diabetes and cancer), behavioral characteristics (e.g. smoking and exercise), exposure to the medical system (e.g. whether the individual gets a regular check-up), and a person's living situation (e.g. having young children in the household). These variables were then used to generate a Random Forest classification model, trained on the 2009-2013 dataset, and tested on the 2014 dataset. We achieved an overall accuracy of 87.8% between the two final models, each using 25 classification trees with bounded depth of 20 nodes, randomly selecting from all 47 variables. With the two proposed policies, this model can be leveraged to efficiently allocate vaccination promotion efforts. Additionally, it can be applied to future surveys, only requiring 3.6% of the variables in the CCHS for successful prediction.

stat.AP↗