As computer vision algorithms are becoming more capable, their applications
in clinical systems will become more pervasive. These applications include
diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and
minimally invasive interventions and surgery, automating instrument motion and
providing image guidance using pre-operative scans. Many of these applications
depend on the specific visual nature of medical scenes and require designing
and applying algorithms to perform in this environment.
In this review, we provide an update to the field of camera-based tracking
and scene mapping in surgery and diagnostics in medical computer vision. We
begin with describing our review process, which results in a final list of 515
papers that we cover. We then give a high-level summary of the state of the art
and provide relevant background for those who need tracking and mapping for
their clinical applications. We then review datasets provided in the field and
the clinical needs therein. Then, we delve in depth into the algorithmic side,
and summarize recent developments, which should be especially useful for
algorithm designers and to those looking to understand the capability of
off-the-shelf methods. We focus on algorithms for deformable environments while
also reviewing the essential building blocks in rigid tracking and mapping
since there is a large amount of crossover in methods. Finally, we discuss the
current state of the tracking and mapping methods along with needs for future
algorithms, needs for quantification, and the viability of clinical
applications in the field. We conclude that new methods need to be designed or
combined to support clinical applications in deformable environments, and more
focus needs to be put into collecting datasets for training and evaluation.

通过对 515 篇文献的审查，我们更新了医学计算机视觉领域中基于相机的手术和诊断中的跟踪和场景建模的状况，并总结了算法的最新发展，重点关注可变形环境下的算法，并讨论了对未来算法的需求、量化的需求以及临床应用的可行性。

医学计算机视觉中的跟踪与建图：综述

Tracking and Mapping in Medical Computer Vision: A Review

Recently, eXplainable AI (XAI) research has focused on counterfactual
explanations as post-hoc justifications for AI-system decisions (e.g. a
customer refused a loan might be told: If you asked for a loan with a shorter
term, it would have been approved). Counterfactuals explain what changes to the
input-features of an AI system change the output-decision. However, there is a
sub-type of counterfactual, semi-factuals, that have received less attention in
AI (though the Cognitive Sciences have studied them extensively). This paper
surveys these literatures to summarise historical and recent breakthroughs in
this area. It defines key desiderata for semi-factual XAI and reports benchmark
tests of historical algorithms (along with a novel, naieve method) to provide a
solid basis for future algorithmic developments.

该论文调查了历史和最近在可解释人工智能 (XAI) 和半事实 (semi-factuals) 领域的突破，定义了半事实 XAI 的主要要求，并报告了历史算法的基准测试（以及一种新颖的，朴素的方法），为未来的算法发展提供了坚实的基础。

半真实 XAI 的先前工作、需求和基准

Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI

Optimization equips engineers and scientists in a variety of fields with the
ability to transcribe their problems into a generic formulation and receive
optimal solutions with relative ease. Industries ranging from aerospace to
robotics continue to benefit from advancements in optimization theory and the
associated algorithmic developments. Nowadays, optimization is used in real
time on autonomous systems acting in safety critical situations, such as
self-driving vehicles. It has become increasingly more important to produce
robust solutions by incorporating uncertainty into optimization programs. This
paper provides a short survey about the state of the art in optimization under
uncertainty. The paper begins with a brief overview of the main classes of
optimization without uncertainty. The rest of the paper focuses on the
different methods for handling both aleatoric and epistemic uncertainty. Many
of the applications discussed in this paper are within the domain of control.
The goal of this survey paper is to briefly touch upon the state of the art in
a variety of different methods and refer the reader to other literature for
more in-depth treatments of the topics discussed here.

本文简要回顾了优化领域中不确定性处理的现状，并聚焦于处理随机性和认知不确定性的不同方法，控制领域的应用占据了重要的地位。