Segmentation of anatomical structures and pathologies is inherently
ambiguous. For instance, structure borders may not be clearly visible or
different experts may have different styles of annotating. The majority of
current state-of-the-art methods do not account for such ambiguities but rather
learn a single mapping from image to segmentation. In this work, we propose a
novel method to model the conditional probability distribution of the
segmentations given an input image. We derive a hierarchical probabilistic
model, in which separate latent variables are responsible for modelling the
segmentation at different resolutions. Inference in this model can be
efficiently performed using the variational autoencoder framework. We show that
our proposed method can be used to generate significantly more realistic and
diverse segmentation samples compared to recent related work, both, when
trained with annotations from a single or multiple annotators.

本文提出了一种基于层次概率模型和变分自编码器框架的新方法，用于建模给定输入图像的分割的条件概率分布，可以有效地解决解剖结构和病理分割中的歧义性问题，同时可以生成比以前的方法更真实和多样化的分割样本。

PHiSeg：医学图像分割中的不确定性捕捉

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

We propose the first practical learned lossless image compression system,
L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and
JPEG 2000. At the core of our method is a fully parallelizable hierarchical
probabilistic model for adaptive entropy coding which is optimized end-to-end
for the compression task. In contrast to recent autoregressive discrete
probabilistic models such as PixelCNN, our method i) models the image
distribution jointly with learned auxiliary representations instead of
exclusively modeling the image distribution in RGB space, and ii) only requires
three forward-passes to predict all pixel probabilities instead of one for each
pixel. As a result, L3C obtains over two orders of magnitude speedups when
sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN).
Furthermore, we find that learning the auxiliary representation is crucial and
outperforms predefined auxiliary representations such as an RGB pyramid
significantly.

我们提出了第一个实用的学习无损图像压缩系统 L3C，它在压缩方面优于流行的人工编码器 PNG、WebP 和 JPEG 2000。L3C 的核心是一种可完全并行化的分层自适应熵编码概率模型，该模型针对压缩任务进行了端到端的优化。与最近的自回归离散概率模型（如 PixelCNN）相比，我们的方法将图像分布与学习的辅助表示一起建模，而不是仅在 RGB 空间中建模图像分布，并且只需要三个前向传递来预测所有像素的概率，而不是为每个像素分别预测一个。此外，我们发现学习辅助表示至关重要，而且显著优于预定义的辅助表示，如 RGB 金字塔。

实用全分辨率学习无损图像压缩

Practical Full Resolution Learned Lossless Image Compression

In probabilistic approaches to classification and information extraction, one
typically builds a statistical model of words under the assumption that future
data will exhibit the same regularities as the training data. In many data
sets, however, there are scope-limited features whose predictive power is only
applicable to a certain subset of the data. For example, in information
extraction from web pages, word formatting may be indicative of extraction
category in different ways on different web pages. The difficulty with using
such features is capturing and exploiting the new regularities encountered in
previously unseen data. In this paper, we propose a hierarchical probabilistic
model that uses both local/scope-limited features, such as word formatting, and
global features, such as word content. The local regularities are modeled as an
unobserved random parameter which is drawn once for each local data set. This
random parameter is estimated during the inference process and then used to
perform classification with both the local and global features--- a procedure
which is akin to automatically retuning the classifier to the local
regularities on each newly encountered web page. Exact inference is intractable
and we present approximations via point estimates and variational methods.
Empirical results on large collections of web data demonstrate that this method
significantly improves performance from traditional models of global features
alone.

本文提出了一种可以同时利用局部和全局特征的分层概率模型，从而提高分类和信息抽取的准确性。通过估计局部数据集中的随机参数，并在推断过程中使用其进行分类，该模型能够自适应地捕捉到新发现的数据中的规律性。实验结果表明，相对于仅使用全局特征的传统模型，本模型能显著提升性能。