Vulvovaginal candidiasis (VVC) is the most prevalent human candidal
infection, estimated to afflict approximately 75% of all women at least once in
their lifetime. It will lead to several symptoms including pruritus, vaginal
soreness, and so on. Automatic whole slide image (WSI) classification is highly
demanded, for the huge burden of disease control and prevention. However, the
WSI-based computer-aided VCC screening method is still vacant due to the scarce
labeled data and unique properties of candida. Candida in WSI is challenging to
be captured by conventional classification models due to its distinctive
elongated shape, the small proportion of their spatial distribution, and the
style gap from WSIs. To make the model focus on the candida easier, we propose
an attention-guided method, which can obtain a robust diagnosis classification
model. Specifically, we first use a pre-trained detection model as prior
instruction to initialize the classification model. Then we design a Skip
Self-Attention module to refine the attention onto the fined-grained features
of candida. Finally, we use a contrastive learning method to alleviate the
overfitting caused by the style gap of WSIs and suppress the attention to false
positive regions. Our experimental results demonstrate that our framework
achieves state-of-the-art performance. Code and example data are available at
this https URL

提出了一种基于注意力引导的方法，利用全幅切片图像分类模型对阴道念珠菌进行诊断分类，通过细粒度特征的自注意模块和对虚假阳性区域的注意力抑制方法，有效地解决了 WSI 图像样式差异并取得了最先进的性能。

整张切片阴道念珠菌感染的渐进式注意力引导筛查

Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis  Screening

Low-light image enhancement is challenging in that it needs to consider not
only brightness recovery but also complex issues like color distortion and
noise, which usually hide in the dark. Simply adjusting the brightness of a
low-light image will inevitably amplify those artifacts. To address this
difficult problem, this paper proposes a novel end-to-end attention-guided
method based on multi-branch convolutional neural network. To this end, we
first construct a synthetic dataset with carefully designed low-light
simulation strategies. The dataset is much larger and more diverse than
existing ones. With the new dataset for training, our method learns two
attention maps to guide the brightness enhancement and denoising tasks
respectively. The first attention map distinguishes underexposed regions from
well lit regions, and the second attention map distinguishes noises from real
textures. With their guidance, the proposed multi-branch
decomposition-and-fusion enhancement network works in an input adaptive way.
Moreover, a reinforcement-net further enhances color and contrast of the output
image. Extensive experiments on multiple datasets demonstrate that our method
can produce high fidelity enhancement results for low-light images and
outperforms the current state-of-the-art methods by a large margin both
quantitatively and visually.

本文提出了一种基于多分支卷积神经网络的新型端到端注意力引导方法，利用合成的低光模拟数据构建数据集，通过使用两个注意力图指导亮度增强和降噪任务，并进一步增强输出图像的色彩和对比度以实现对低光图像的高保真增强，该方法在多个数据集上表现优异，比当前最先进的方法有更好的定量和视觉效果。