55 research outputs found
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Skin cancer is the most common malignancy in the world. Automated skin cancer
detection would significantly improve early detection rates and prevent deaths.
To help with this aim, a number of datasets have been released which can be
used to train Deep Learning systems - these have produced impressive results
for classification. However, this only works for the classes they are trained
on whilst they are incapable of identifying skin lesions from previously unseen
classes, making them unconducive for clinical use. We could look to massively
increase the datasets by including all possible skin lesions, though this would
always leave out some classes. Instead, we evaluate Siamese Neural Networks
(SNNs), which not only allows us to classify images of skin lesions, but also
allow us to identify those images which are different from the trained classes
- allowing us to determine that an image is not an example of our training
classes. We evaluate SNNs on both dermoscopic and clinical images of skin
lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on
clinical and dermoscopic datasets, respectively. Although this is slightly
lower than the state-of-the-art results, the SNN approach has the advantage
that it can detect out-of-class examples. Our results highlight the potential
of an SNN approach as well as pathways towards future clinical deployment.Comment: 10 pages, 5 figures, 5 table
Long-term Reproducibility for Neural Architecture Search
It is a sad reflection of modern academia that code is often ignored after
publication -- there is no academic 'kudos' for bug fixes / maintenance. Code
is often unavailable or, if available, contains bugs, is incomplete, or relies
on out-of-date / unavailable libraries. This has a significant impact on
reproducibility and general scientific progress. Neural Architecture Search
(NAS) is no exception to this, with some prior work in reproducibility.
However, we argue that these do not consider long-term reproducibility issues.
We therefore propose a checklist for long-term NAS reproducibility. We evaluate
our checklist against common NAS approaches along with proposing how we can
retrospectively make these approaches more long-term reproducible.Comment: 4 pages, LaTeX, Typos correcte
Predicting the Performance of a Computing System with Deep Networks
Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance of hardware largely focus around benchmarking – leveraging standardised workloads which seek to be representative of an end-user’s needs. Two key challenges are present; benchmark workloads may not be representative of an end-user’s workload, and benchmark scores are not easily obtained for all hardware. Within this paper, we demonstrate the potential to build Deep Learning models to predict benchmark scores for unseen hardware. We undertake our evaluation with the openly available SPEC 2017 benchmark results. We evaluate three different networks, one fully-connected network along with two Convolutional Neural Networks (one bespoke and one ResNet inspired) and demonstrate impressive 2 scores of 0.96, 0.98 and 0.94 respectively
Analysis of power-saving techniques over a large multi-use cluster with variable workload
Reduction of power consumption for any computer system is now an important issue, although this should be carried out in a manner that is not detrimental to the users of that computer system. We present a number of policies that can be applied to multi-use clusters where computers are shared between interactive users and high-throughput computing. We evaluate policies by trace-driven simulations to determine the effects on power consumed by the high-throughput workload and impact on high-throughput users. We further evaluate these policies for higher workloads by synthetically generating workloads based around the profiled workload observed through our system. We demonstrate that these policies could save 55% of the currently used energy for our high-throughput jobs over our current cluster policies without affecting the high-throughput users’ experience
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