Robust and accurate segmentation for elongated physiological structures is
challenging, especially in the ambiguous region, such as the corneal
endothelium microscope image with uneven illumination or the fundus image with
disease interference. In this paper, we present a spatial and scale
uncertainty-aware network (SSU-Net) that fully uses both spatial and scale
uncertainty to highlight ambiguous regions and integrate hierarchical structure
contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps
using Monte Carlo dropout to approximate Bayesian networks. Based on these
spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA)
module to guide the model to focus on ambiguous regions. Second, we extract the
uncertainty under different scales and propose the multi-scale
uncertainty-aware (MSUA) fusion module to integrate structure contexts from
hierarchical predictions, strengthening the final prediction. Finally, we
visualize the uncertainty map of final prediction, providing interpretability
for segmentation results. Experiment results show that the SSU-Net performs
best on cornea endothelial cell and retinal vessel segmentation tasks.
Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more
accurate and robust.

提出了一种空间和尺度不确定性感知网络（SSU-Net），它可以应对生理结构中的语义歧义区域，从而提高了分割的准确性和鲁棒性。实验结果表明，与同类基于不确定性的方法相比，SSU-Net 具有更高的分割性能。

利用空间和尺度不确定性感知网络对生理结构进行拉长分割

Elongated Physiological Structure Segmentation via Spatial and Scale  Uncertainty-aware Network

Humans can make predictions on various time scales and hierarchical levels.
Thereby, the learning of event encodings seems to play a crucial role. In this
work we model the development of hierarchical predictions via autonomously
learned latent event codes. We present a hierarchical recurrent neural network
architecture, whose inductive learning biases foster the development of
sparsely changing latent state that compress sensorimotor sequences. A higher
level network learns to predict the situations in which the latent states tend
to change. Using a simulated robotic manipulator, we demonstrate that the
system (i) learns latent states that accurately reflect the event structure of
the data, (ii) develops meaningful temporal abstract predictions on the higher
level, and (iii) generates goal-anticipatory behavior similar to gaze behavior
found in eye-tracking studies with infants. The architecture offers a step
towards the autonomous learning of compressed hierarchical encodings of
gathered experiences and the exploitation of these encodings to generate
adaptive behavior.

通过自主学习的潜在事件编码，我们模拟了层次预测的发展，并提出了一种采用递归神经网络的分层体系结构，能够生成能够压缩感知动作序列的稀疏变化潜在状态。高层网络能预测潜在状态发生变化的情况。我们在仿真机器人操纵器上进行了实验，结果显示这个系统学习到了准确的数据事件结构，能够生成类似于婴儿眼追踪研究中发现的目标预期行为，并且为自主学习压缩层次编码和利用这些编码来生成自适应行为提供了新的方式。

基于神经网络事件分割的层次预期建立

Developing hierarchical anticipations via neural network-based event segmentation

Long-tail recognition tackles the natural non-uniformly distributed data in
real-world scenarios. While modern classifiers perform well on populated
classes, its performance degrades significantly on tail classes. Humans,
however, are less affected by this since, when confronted with uncertain
examples, they simply opt to provide coarser predictions. Motivated by this, a
deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to
the long-tail problem, combining realism with hierarchical predictions. The
model has the option to reject classifying samples at different levels of the
taxonomy, once it cannot guarantee the desired performance. Deep-RTC is
implemented with a stochastic tree sampling during training to simulate all
possible classification conditions at finer or coarser levels and a rejection
mechanism at inference time. Experiments on the long-tailed version of four
datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the
proposed approach preserves more information on all classes with different
popularity levels. Deep-RTC also outperforms the state-of-the-art methods in
longtailed recognition, hierarchical classification, and learning with
rejection literature using the proposed correctly predicted bits (CPB) metric.

提出了一种新的解决长尾问题的方法 —— 深度逼真的分类器（Deep-RTC），该模型将现实主义与分层预测相结合，并具有在分类不确定性时拒绝样本的机制。