Brain stroke has become a significant burden on global health and thus we
need remedies and prevention strategies to overcome this challenge. For this,
the immediate identification of stroke and risk stratification is the primary
task for clinicians. To aid expert clinicians, automated segmentation models
are crucial. In this work, we consider the publicly available dataset ATLAS
$v2.0$ to benchmark various end-to-end supervised U-Net style models.
Specifically, we have benchmarked models on both 2D and 3D brain images and
evaluated them using standard metrics. We have achieved the highest Dice score
of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net
respectively. We have conducted the Wilcoxon test for 3D models to correlate
the relationship between predicted and actual stroke volume. For
reproducibility, the code and model weights are made publicly available:
this https URL

脑卒中已经成为全球健康负担，因此我们需要治疗方法和预防策略来克服这一挑战。在此工作中，我们考虑了公开可用的数据集 ATLAS v2.0，以评估各种端到端有监督的 U-Net 风格模型。我们在 2D 变换器模型上达到了最高的 Dice 得分 0.583，在 3D 残差 U-Net 模型上达到了 0.504。我们对 3D 模型进行了 Wilcoxon 测试，以确定预测和实际中风体积之间的关系。为了可复现性，代码和模型权重已公开提供。

BeSt-LeS：使用深度监督进行脑卒中损伤分割的基准评估

BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision

Citrus diseases have been major issues for citrus growing worldwide for many
years they can lead significantly reduce fruit quality. the most harmful citrus
diseases are citrus canker, citrus greening, citrus black spot, citrus leaf
miner which can have significant economic losses of citrus industry in
worldwide prevention and management strategies like chemical treatments. Citrus
diseases existing in all over the world where citrus is growing its effects the
citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus
diseases is highly impact on economic factor that can also produce low quality
fruits and increased the rate for diseases management. Sanitation and routine
monitoring can be effective in managing certain citrus diseases, but others may
require more intensive treatments like chemical or biological control methods.

针对全球柑橘种植业多年来主要存在柑橘疾病问题，如柑橘溃疡病、黄龙病、柑橘黑斑病、柑橘叶蛀虫等，影响了柑橘果实品质，引起了严重的经济损失和疾病管理问题，为了柑橘行业的健康发展提出了多种化学和生物防治方法、定期监测以及最大限度地保证卫生情况等预防和管理策略。

使用机器学习和深度学习预测柑橘病害：分类器，模型 SLR

Prediction of Citrus Diseases Using Machine Learning And Deep Learning:  Classifier, Models SLR

Epidemics of infectious diseases are an important threat to public health and
global economies. Yet, the development of prevention strategies remains a
challenging process, as epidemics are non-linear and complex processes. For
this reason, we investigate a deep reinforcement learning approach to
automatically learn prevention strategies in the context of pandemic influenza.
Firstly, we construct a new epidemiological meta-population model, with 379
patches (one for each administrative district in Great Britain), that
adequately captures the infection process of pandemic influenza. Our model
balances complexity and computational efficiency such that the use of
reinforcement learning techniques becomes attainable. Secondly, we set up a
ground truth such that we can evaluate the performance of the 'Proximal Policy
Optimization' algorithm to learn in a single district of this epidemiological
model. Finally, we consider a large-scale problem, by conducting an experiment
where we aim to learn a joint policy to control the districts in a community of
11 tightly coupled districts, for which no ground truth can be established.
This experiment shows that deep reinforcement learning can be used to learn
mitigation policies in complex epidemiological models with a large state space.
Moreover, through this experiment, we demonstrate that there can be an
advantage to consider collaboration between districts when designing prevention
strategies.

本文使用深度强化学习方法，针对流感大流行背景下预防策略自动学习的挑战性进程，建立新的流行病学元群体模型，利用极近政策优化算法学习单一区域的预防策略，并在 11 个区域的联合控制 policy 中考虑区域间的协作，从而有效应对具有大状态空间的复杂流行病学模型。