Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical
cancer screening test in resource-constrained settings of low- and
middle-income countries (LMICs), which are often performed screening camps or
primary/community health centers by nurses instead of the preferred but
unavailable expert Gynecologist. To address the highly subjective nature of the
test, various handheld devices integrating cameras or smartphones have been
recently explored to capture cervical images during VIA and aid decision-making
via telemedicine or AI models. Most studies proposing AI models retrospectively
use a relatively small number of already collected images from specific
devices, digital cameras, or smartphones; the challenges and protocol for
quality image acquisition during VIA in resource-constrained camp settings,
challenges in getting gold standard, data imbalance, etc. are often overlooked.
We present a novel approach and describe the end-to-end design process to build
a robust smartphone-based AI-assisted system that does not require buying a
separate integrated device: the proposed protocol for quality image acquisition
in resource-constrained settings, dataset collected from 1,430 women during VIA
performed by nurses in screening camps, preprocessing pipeline, and training
and evaluation of a deep-learning-based classification model aimed to identify
(pre)cancerous lesions. Our work shows that the readily available smartphones
and a suitable protocol can capture the cervix images with the required details
for the VIA test well; the deep-learning-based classification model provides
promising results to assist nurses in VIA screening; and provides a direction
for large-scale data collection and validation in resource-constrained
settings.

通过使用智能手机等手持设备，采集可靠的宫颈图像并结合人工智能辅助技术，我们在资源受限的环境中提出了一个可行的宫颈癌筛查方案，该方案可以协助护士进行癌前病变的识别和筛查。

人工智能辅助宫颈癌筛查

AI-Assisted Cervical Cancer Screening

Retinal vessel segmentation is generally grounded in image-based datasets
collected with bench-top devices. The static images naturally lose the dynamic
characteristics of retina fluctuation, resulting in diminished dataset
richness, and the usage of bench-top devices further restricts dataset
scalability due to its limited accessibility. Considering these limitations, we
introduce the first video-based retinal dataset by employing handheld devices
for data acquisition. The dataset comprises 635 smartphone-based fundus videos
collected from four different clinics, involving 415 patients from 50 to 75
years old. It delivers comprehensive and precise annotations of retinal
structures in both spatial and temporal dimensions, aiming to advance the
landscape of vasculature segmentation. Specifically, the dataset provides three
levels of spatial annotations: binary vessel masks for overall retinal
structure delineation, general vein-artery masks for distinguishing the vein
and artery, and fine-grained vein-artery masks for further characterizing the
granularities of each artery and vein. In addition, the dataset offers temporal
annotations that capture the vessel pulsation characteristics, assisting in
detecting ocular diseases that require fine-grained recognition of hemodynamic
fluctuation. In application, our dataset exhibits a significant domain shift
with respect to data captured by bench-top devices, thus posing great
challenges to existing methods. In the experiments, we provide evaluation
metrics and benchmark results on our dataset, reflecting both the potential and
challenges it offers for vessel segmentation tasks. We hope this challenging
dataset would significantly contribute to the development of eye disease
diagnosis and early prevention.

本文介绍了一种采用手机作为数据采集设备的视觉调节分类视频数据集，通过空间和时间注释，在血管分割领域有着广泛而精确的应用，并希望对眼部疾病的诊断和早期预防有所贡献。

基于便携式设备的眼底视频数据集用于视网膜血管分割

RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel  Segmentation

Depth perception is paramount to tackle real-world problems, ranging from
autonomous driving to consumer applications. For the latter, depth estimation
from a single image represents the most versatile solution, since a standard
camera is available on almost any handheld device. Nonetheless, two main issues
limit its practical deployment: i) the low reliability when deployed
in-the-wild and ii) the demanding resource requirements to achieve real-time
performance, often not compatible with such devices. Therefore, in this paper,
we deeply investigate these issues showing how they are both addressable
adopting appropriate network design and training strategies -- also outlining
how to map the resulting networks on handheld devices to achieve real-time
performance. Our thorough evaluation highlights the ability of such fast
networks to generalize well to new environments, a crucial feature required to
tackle the extremely varied contexts faced in real applications. Indeed, to
further support this evidence, we report experimental results concerning
real-time depth-aware augmented reality and image blurring with smartphones
in-the-wild.

探究采用适当的网络设计和训练策略以及如何将结果网络映射到手持设备以实现实时性能的方式解决单张图像深度估计的低可靠性和需求高计算资源的问题，并通过实验证明这些快速网络具有良好的泛化能力以用于现实应用，包括野外中的实时深度感知增强现实和图像模糊处理。