5 accepted papers
Saliency methods are critical tools that allow the estimation of the most important features of an input image that contribute to the network’s prediction. These tools are pivotal in high-stakes applications such as medical diagnosis or autonomous driving. Additionally, these tools can help identify…
There are strong incentives to build classification systems that show outstanding performance on various datasets and benchmarks. This can encourage a narrow focus on models and the performance metrics used to evaluate and compare them—resulting in a growing body of literature to evaluate and compar…
In this paper we investigate multiple instance learning (MIL), using generic tile-based spatio-temporal features, for the classification of benign and malignant lesions in breast cancer magnetic resonance imaging (MRI). In particular, we compare the performance of citation-kNN (CkNN) and conventiona…
We determine the power spectrum of an ideal neuron which encodes information using a pulse interval modulation scheme in continuous time. We develop this by considering the rigorous derivation of the Digital Pulse Interval Modulation (DPIM) coding scheme spectra of L. Vangelista et al. in the limit…
In this paper we demonstrate by simulation, that the spike features apparent in low-impedance deep brain stimulation (DBS) targeting microelectrode recordings (MER) may not reflect the action potentials of individual neurons. Rather, they are more likely to be compound action potentials from a synch…