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.

NYCTALE 提出了一个神经启发和基于证据积累的 Transformer 架构，为肺癌诊断的个性化医学领域提供了一种新的路径。与传统的基于计算机断层扫描（CT）的深度学习模型不同，NYCTALE 仅在积累足够证据时进行预测。初步实验结果表明，即使在这个具有挑战性的小数据集上使用约 60％较少的训练数据，NYCTALE 的表现优于基准准确度。

NYCTALE: 面向肺部结节侵袭性预测的神经证据转换器

NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung  Nodule Invasiveness Prediction

Deep neural networks (DNNs) struggle to learn in dynamic environments since
they rely on fixed datasets or stationary environments. Continual learning (CL)
aims to address this limitation and enable DNNs to accumulate knowledge
incrementally, similar to human learning. Inspired by how our brain
consolidates memories, a powerful strategy in CL is replay, which involves
training the DNN on a mixture of new and all seen classes. However, existing
replay methods overlook two crucial aspects of biological replay: 1) the brain
replays processed neural patterns instead of raw input, and 2) it prioritizes
the replay of recently learned information rather than revisiting all past
experiences. To address these differences, we propose SHARP, an efficient
neuro-inspired CL method that leverages sparse dynamic connectivity and
activation replay. Unlike other activation replay methods, which assume layers
not subjected to replay have been pretrained and fixed, SHARP can continually
update all layers. Also, SHARP is unique in that it only needs to replay few
recently seen classes instead of all past classes. Our experiments on five
datasets demonstrate that SHARP outperforms state-of-the-art replay methods in
class incremental learning. Furthermore, we showcase SHARP's flexibility in a
novel CL scenario where the boundaries between learning episodes are blurry.
The SHARP code is available at
https://github.com/BurakGurbuz97/SHARP-Continual-Learning.

提出了一种神经启发的连续学习（CL）方法（SHARP），该方法利用稀疏动态连接和激活重播来解决现有的重播方法忽略生物重播的两个关键方面的问题。 实验证明，SHARP 在类增量学习方面优于现有的重播方法。