Designing deep learning algorithms for gland segmentation is crucial for
automatic cancer diagnosis and prognosis, yet the expensive annotation cost
hinders the development and application of this technology. In this paper, we
make a first attempt to explore a deep learning method for unsupervised gland
segmentation, where no manual annotations are required. Existing unsupervised
semantic segmentation methods encounter a huge challenge on gland images: They
either over-segment a gland into many fractions or under-segment the gland
regions by confusing many of them with the background. To overcome this
challenge, our key insight is to introduce an empirical cue about gland
morphology as extra knowledge to guide the segmentation process. To this end,
we propose a novel Morphology-inspired method via Selective Semantic Grouping.
We first leverage the empirical cue to selectively mine out proposals for gland
sub-regions with variant appearances. Then, a Morphology-aware Semantic
Grouping module is employed to summarize the overall information about the
gland by explicitly grouping the semantics of its sub-region proposals. In this
way, the final segmentation network could learn comprehensive knowledge about
glands and produce well-delineated, complete predictions. We conduct
experiments on GlaS dataset and CRAG dataset. Our method exceeds the
second-best counterpart over 10.56% at mIOU.

设计深度学习算法用于腺体分割对于自动癌症诊断和预后至关重要，然而昂贵的注释成本阻碍了该技术的发展和应用。本文首次尝试探索用于无监督腺体分割的深度学习方法，无需手动注释。为了克服语义分割方法在腺体图像上遇到的挑战，我们引入了关于腺体形态的经验性线索作为额外知识来指导分割过程。我们提出了一种新颖的基于形态学启发的选择性语义分组方法，通过该方法，最终分割网络能够学习关于腺体的综合知识，并产生清晰完整的预测。在 GlaS 和 CRAG 数据集上进行实验，我们的方法在 mIOU 上超过次优方法 10.56%。

基于形态学启发的无监督腺体分割：选择性语义分组

Morphology-inspired Unsupervised Gland Segmentation via Selective  Semantic Grouping

In this paper, we tackle the problem of learning visual representations from
unlabeled scene-centric data. Existing works have demonstrated the potential of
utilizing the underlying complex structure within scene-centric data; still,
they commonly rely on hand-crafted objectness priors or specialized pretext
tasks to build a learning framework, which may harm generalizability. Instead,
we propose contrastive learning from data-driven semantic slots, namely
SlotCon, for joint semantic grouping and representation learning. The semantic
grouping is performed by assigning pixels to a set of learnable prototypes,
which can adapt to each sample by attentive pooling over the feature and form
new slots. Based on the learned data-dependent slots, a contrastive objective
is employed for representation learning, which enhances the discriminability of
features, and conversely facilitates grouping semantically coherent pixels
together. Compared with previous efforts, by simultaneously optimizing the two
coupled objectives of semantic grouping and contrastive learning, our approach
bypasses the disadvantages of hand-crafted priors and is able to learn
object/group-level representations from scene-centric images. Experiments show
our approach effectively decomposes complex scenes into semantic groups for
feature learning and significantly benefits downstream tasks, including object
detection, instance segmentation, and semantic segmentation. Code is available
at: this https URL

本文探讨了从无标签场景中心数据中学习视觉表示的问题，提出了一种的基于对比学习和数据驱动语义槽的语义聚类和表示学习方法，通过此方法能够更好的区分特征和语义相符的像素点，以此来有效地分解场景中的像素群组，并显著提高目标检测、实例分割和语义分割等下游应用的效果。

利用语义分组进行自监督视觉表示学习

Self-Supervised Visual Representation Learning with Semantic Grouping

Photorealism is a complex concept that cannot easily be formulated
mathematically. Deep Photo Style Transfer is an attempt to transfer the style
of a reference image to a content image while preserving its photorealism. This
is achieved by introducing a constraint that prevents distortions in the
content image and by applying the style transfer independently for semantically
different parts of the images. In addition, an automated segmentation process
is presented that consists of a neural network based segmentation method
followed by a semantic grouping step. To further improve the results a measure
for image aesthetics is used and elaborated. If the content and the style image
are sufficiently similar, the result images look very realistic. With the
automation of the image segmentation the pipeline becomes completely
independent from any user interaction, which allows for new applications.

该研究介绍了一种基于深度学习技术的图像风格转换方法，通过引入内容图像的失真约束条件，应用于图像语义不同的部分，使用基于神经网络的图像分割方法和语义分组步骤进行自动分割处理，进一步提高了图像的美感效果，并且使得整个流程不依赖于用户交互的干预，具有广泛的应用前景。