NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung
  Nodule Invasiveness Prediction
Sadaf Khademi Anastasia Oikonomou Konstantinos N. Plataniotis Arash Mohammadi
Drawing inspiration from the primate brain's intriguing evidence accumulation
process, and guided by models from cognitive psychology and neuroscience, the
paper introduces the NYCTALE framework, a neuro-inspired and evidence
accumulation-based Transformer architecture. The proposed neuro-inspired
NYCTALE offers a novel pathway in the domain of Personalized Medicine (PM) for
lung cancer diagnosis. In nature, Nyctales are small owls known for their
nocturnal behavior, hunting primarily during the darkness of night. The NYCTALE
operates in a similarly vigilant manner, i.e., processing data in an
evidence-based fashion and making predictions dynamically/adaptively. Distinct
from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the
NYCTALE performs predictions only when sufficient amount of evidence is
accumulated. In other words, instead of processing all or a pre-defined subset
of CT slices, for each person, slices are provided one at a time. The NYCTALE
framework then computes an evidence vector associated with contribution of each
new CT image. A decision is made once the total accumulated evidence surpasses
a specific threshold. Preliminary experimental analyses conducted using a
challenging in-house dataset comprising 114 subjects. The results are
noteworthy, suggesting that NYCTALE outperforms the benchmark accuracy even
with approximately 60% less training data on this demanding and small dataset.