arXiv subjects

Keith Miller

Publications and source records attributed to Keith Miller.

4 recordsLinked to original sources

arXiv2026

G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation

We introduce Graph Neural Automata Clustering (G-NAC), an unsupervised clustering method in which observations interact as cells on a fixed neighborhood graph. A shared recurrent graph-neural cellular rule evolves latent domain states through local interactions, which are converted into a rank-based spectral affinity for partitioning. Across 73 clustering tasks from 57 benchmark datasets, G-NAC achieved a mean adjusted Rand index (ARI) of 0.7951, comparable to Genie at 0.7941 and higher than the other evaluated baselines. Empirical training time and GPU memory scaled approximately linearly from 5,000 to 100,000 nodes. Learned transition rules also transferred from smaller source graphs to independent 100,000-node samples generated under matched conditions. These results demonstrate a recurrent graph-clustering formulation while identifying dependencies on graph quality, readout design, and source-target similarity.

cs.LG↗

arXiv2026

Toward Artifact-Free Dermoscopy: Characterization of ISIC 2019 Image Defects for Deep Learning Applications

The International Skin Imaging Collaboration (ISIC) archive offers the largest publicly available collection of dermoscopic images for skin lesion analysis. However, the ISIC 2019 dataset suffers from several acquisition artifacts such as hair occlusions, vignetting, surgical ink markings, and ruler scales, all of which compromise its utility in deep learning workflows by introducing non-biological visual structures. To address these limitations, we present a rigorously annotated version of ISIC 2019 that includes binary masks for key artifact types. Ink marks and vignetting were annotated through systematic manual review and iterative refinement using professional image editing software. For hair and ruler artifacts, we implemented a semi-automatic pipeline based on our previously developed deep learning architecture, ChimeraNet. Automated predictions were then manually corrected as needed for precision. The resulting dataset supports both the development of our own melanoma detection model and broader community research requiring artifact-mitigated dermoscopic images.

eess.IV↗

arXiv2025

Cepstrum-Based Texture Features for Melanoma Detection

This paper introduces a set of cepstrum-based texture features for melanoma classification and validates their performance on dermoscopic images from the ISIC 2019 dataset. We propose applying gray-level co-occurrence matrix (GLCM) statistics to 2D cepstral representations, a novel approach in image analysis. Combined with established handcrafted lesion descriptors, these features were evaluated using XGBoost models. Incorporating select cepstral features improved the area under the receiver operating characteristic curve, accuracy, and F1 score for binary melanoma vs. nevus classification. Results suggest that cepstral GLCM features offer complementary discriminatory information for melanoma detection.

eess.IV↗

arXiv2022

An Improved Volumetric Metric for Quantum Computers via more Representative Quantum Circuit Shapes

In this work, we propose a generalization of the current most widely used quantum computing hardware metric known as the quantum volume. The quantum volume specifies a family of random test circuits defined such that the logical circuit depth is equal to the total number of qubits used in the computation. However, such square circuit shapes do not directly relate to many specific applications for which one may wish to use a quantum computer. Based on surveying available resource estimates for known quantum algorithms, we generalize the quantum volume to a handful of representative circuit shapes, which we call Quantum Volumetric Classes, based on the scaling behavior of the logical circuit depth (time) with the problem size (qubit number).

quant-ph↗