Consistency learning is a central strategy to tackle unlabeled data in
semi-supervised medical image segmentation (SSMIS), which enforces the model to
produce consistent predictions under the perturbation. However, most current
approaches solely focus on utilizing a specific single perturbation, which can
only cope with limited cases, while employing multiple perturbations
simultaneously is hard to guarantee the quality of consistency learning. In
this paper, we propose an Adaptive Bidirectional Displacement (ABD) approach to
solve the above challenge. Specifically, we first design a bidirectional patch
displacement based on reliable prediction confidence for unlabeled data to
generate new samples, which can effectively suppress uncontrollable regions and
still retain the influence of input perturbations. Meanwhile, to enforce the
model to learn the potentially uncontrollable content, a bidirectional
displacement operation with inverse confidence is proposed for the labeled
images, which generates samples with more unreliable information to facilitate
model learning. Extensive experiments show that ABD achieves new
state-of-the-art performances for SSMIS, significantly improving different
baselines. Source code is available at this https URL

提出了一种自适应的双向位移方法 (ABD) 来解决半监督医学图像分割中不可标记数据一致性学习的挑战，包括可靠预测置信度的双向补丁位移生成新样本以有效抑制不可控区域和保留输入扰动的影响，以及通过逆置信度生成具有更不可靠信息的样本以促进模型学习。大量实验证明，ABD 在半监督医学图像分割方面取得了新的最佳性能，显著改进了不同基准模型。

自适应双向位移用于半监督医学图像分割

Adaptive Bidirectional Displacement for Semi-Supervised Medical Image  Segmentation

Large language models (LLM) are generating information at a rapid pace,
requiring users to increasingly rely and trust the data. Despite remarkable
advances of LLM, Information generated by LLM is not completely trustworthy,
due to challenges in information quality. Specifically, integrity of
Information quality decreases due to unreliable, biased, tokenization during
pre-training of LLM. Moreover, due to decreased information quality issues, has
led towards hallucination, fabricated information. Unreliable information can
lead towards flawed decisions in businesses, which impacts economic activity.
In this work, we introduce novel mathematical information quality evaluation of
LLM, we furthermore analyze and highlight information quality challenges,
scaling laws to systematically scale language models.

大型语言模型产生信息速度快，但信息质量不可信，由于预训练中的不可靠和有偏见的标记化导致信息质量下降，进而导致虚构和伪造信息。本文引入了一种新的数学信息质量评估方法，并分析了信息质量挑战和语言模型的可伸缩性规律。