Dr Spencer Thomas

Senior Lecturer in Data Science and Computational Intelligence
MPhys PhD


Areas of specialism

Data Science; Machine Learning; Artificial Intelligence; Computational Intelligence; Healthcare Data ; Large and High Dimensional Data Analysis


Research interests


Postgraduate research supervision




F. Ntelemis, Y. Jin and S. A. Thomas, "Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3085125.

S. A. Thomas, F. Brochu, A framework for traceble storage and curation of measurement data, Measurement: Sensors, 2021 1016/j.measen.2021.100201.

A. Lemanska, S. A. Thomas, et al, Study into COVID-19 Crisis Using Primary Care Mental Health Consultations and Prescriptions Data, Studies in Health Technology and Informatics. 2021 doi: 10.3233/SHTI210277

TARANPREET RAI, AMBRA MORISI, BARBARA BACCI, NICHOLAS JAMES BACON, Michael J. Dark, Tawfik Aboellail, SPENCER A THOMAS, MIROSLAW Z BOBER, ROBERTO MARCELLO LA RAGIONE, KEVIN WELLS (2022)Deep learning for necrosis detection using canine perivascular wall tumour whole slide images, In: Scientific reports1210634

Abstract Necrosis seen in histopathology Whole Slide Images is a major criterion that contributes towards scoring tumour grade which then determines treatment options. However conventional manual assessment suffers from inter-operator reproducibility impacting grading precision. To address this, automatic necrosis detection using AI may be used to assess necrosis for final scoring that contributes towards the final clinical grade. Using deep learning AI, we describe a novel approach for automating necrosis detection in Whole Slide Images, tested on a canine Soft Tissue Sarcoma (cSTS) data set consisting of canine Perivascular Wall Tumours (cPWTs). A patch-based deep learning approach was developed where different variations of training a DenseNet-161 Convolutional Neural Network architecture were investigated as well as a stacking ensemble. An optimised DenseNet-161 with post-processing produced a hold-out test F1-score of 0.708 demonstrating state-of-the-art performance. This represents a novel first-time automated necrosis detection method in the cSTS domain as well specifically in detecting necrosis in cPWTs demonstrating a significant step forward in reproducible and reliable necrosis assessment for improving the precision of tumour grading.

—The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved , multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging modalities with optical microscopy and immunohistochemistry, enhancing our understanding of the biological mechanisms and progression of diseases. Techniques such as imaging mass cy-tometry provide labelled multidimensional (multiplex) images of specific components used in conjunction with digital pathology techniques. These powerful techniques generate a wealth of high dimensional data that create significant challenges in data analysis. Unsupervised methods such as clustering are an attractive way to analyse these data, however, they require the selection of parameters such as the number of clusters. Here we propose a methodology to estimate the number of clusters in an automatic data-driven manner using a deep sparse autoencoder to embed the data into a lower dimensional space. We compute the density of regions in the embedded space, the majority of which are empty, enabling the high density regions (i.e. clusters) to be detected as outliers and provide an estimate for the number of clusters. This framework provides a fully unsupervised and data-driven method to analyse multidimensional data. In this work we demonstrate our method using 45 multiplex imaging mass cytometry datasets. Moreover, our model is trained using only one of the datasets and the learned embedding is applied to the remaining 44 images providing an efficient process for data analysis. Finally, we demonstrate the high computational efficiency of our method which is two orders of magnitude faster than estimating via computing the sum squared distances as a function of cluster number.

Additional publications