Medical image analysis using deep learning is often challenged by limited
labeled data and high annotation costs. Fine-tuning the entire network in
label-limited scenarios can lead to overfitting and suboptimal performance.
Recently, prompt tuning has emerged as a more promising technique that
introduces a few additional tunable parameters as prompts to a task-agnostic
pre-trained model, and updates only these parameters using supervision from
limited labeled data while keeping the pre-trained model unchanged. However,
previous work has overlooked the importance of selective labeling in downstream
tasks, which aims to select the most valuable downstream samples for annotation
to achieve the best performance with minimum annotation cost. To address this,
we propose a framework that combines selective labeling with prompt tuning
(SLPT) to boost performance in limited labels. Specifically, we introduce a
feature-aware prompt updater to guide prompt tuning and a TandEm Selective
LAbeling (TESLA) strategy. TESLA includes unsupervised diversity selection and
supervised selection using prompt-based uncertainty. In addition, we propose a
diversified visual prompt tuning strategy to provide multi-prompt-based
discrepant predictions for TESLA. We evaluate our method on liver tumor
segmentation and achieve state-of-the-art performance, outperforming
traditional fine-tuning with only 6% of tunable parameters, also achieving 94%
of full-data performance by labeling only 5% of the data.

使用深度学习进行医学图像分析时，由于有限的标记数据和高昂的注释成本，往往面临挑战。本文提出了一种将选择性标记与提示调整相结合的框架（SLPT），以提高有限标签下的性能，同时保持预训练模型不变，通过仅使用有限标记数据对这些参数进行更新。该框架包括了一个特征感知提示更新器，以引导提示调整，并采用基于提示的不确定性的无监督多样性选择和有监督选择。此外，我们提出了一种多样化的视觉提示调整策略，为选择性标记提供多提示的不同预测。在肝肿瘤分割任务上，我们的方法取得了最先进的性能，仅使用可调参数的 6%，仅标记了 5% 的数据即可达到全数据性能的 94%。

SLPT：基于限制标签的病变分割的选择性标定与提示调整

SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion  Segmentation

Dynamic learning systems subject to selective labeling exhibit censoring,
i.e. persistent negative predictions assigned to one or more subgroups of
points. In applications like consumer finance, this results in groups of
applicants that are persistently denied and thus never enter into the training
data. In this work, we formalize censoring, demonstrate how it can arise, and
highlight difficulties in detection. We consider safeguards against censoring -
recourse and randomized-exploration - both of which ensure we collect labels
for points that would otherwise go unobserved. The resulting techniques allow
examples from censored groups to enter into the training data and correct the
model. Our results highlight the otherwise unmeasured harms of censoring and
demonstrate the effectiveness of mitigation strategies across a range of data
generating processes.

在动态学习系统中，选择性标记会导致检查，即对一个或多个子组中分配的持续负面预测。我们正式化了检查，展示了它如何出现，并强调了检查检测的困难。我们考虑对检查的保障 - 救济和随机探索 - 两者都确保我们收集标签以观察到否则不会被观察到的点。结果技术允许来自被审查小组的示例进入培训数据并更正模型，我们的结果突显了检查的未测量危害，并证明了在一系列数据生成过程中缓解策略的有效性。

动态学习系统中的算法审查

Algorithmic Censoring in Dynamic Learning Systems

A key bottleneck in building automatic extraction models for visually rich
documents like invoices is the cost of acquiring the several thousand
high-quality labeled documents that are needed to train a model with acceptable
accuracy. We propose Selective Labeling to simplify the labeling task to
provide "yes/no" labels for candidate extractions predicted by a model trained
on partially labeled documents. We combine this with a custom active learning
strategy to find the predictions that the model is most uncertain about. We
show through experiments on document types drawn from 3 different domains that
selective labeling can reduce the cost of acquiring labeled data by $10\times$
with a negligible loss in accuracy.

提出使用选择性标注结合主动学习的方法，以简化对可预测提取的样本进行标注的成本，实验证明相比全额标注，该方法可将成本降低 10 倍同时精度不受影响，并且适用于不同领域的文档。