Creating large-scale and well-annotated datasets to train AI algorithms is
crucial for automated tumor detection and localization. However, with limited
resources, it is challenging to determine the best type of annotations when
annotating massive amounts of unlabeled data. To address this issue, we focus
on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;
both applications require significant effort and time for pixel-wise annotation
due to the high dimensional nature of the data, involving either temporary or
spatial dimensions. In this paper, we develop a new annotation strategy, termed
Drag&Drop, which simplifies the annotation process to drag and drop. This
annotation strategy is more efficient, particularly for temporal and volumetric
imaging, than other types of weak annotations, such as per-pixel, bounding
boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop
annotations, we develop a novel weakly supervised learning method based on the
watershed algorithm. Experimental results show that our method achieves better
detection and localization performance than alternative weak annotations and,
more importantly, achieves similar performance to that trained on detailed
per-pixel annotations. Interestingly, we find that, with limited resources,
allocating weak annotations from a diverse patient population can foster models
more robust to unseen images than allocating per-pixel annotations for a small
set of images. In summary, this research proposes an efficient annotation
strategy for tumor detection and localization that is less accurate than
per-pixel annotations but useful for creating large-scale datasets for
screening tumors in various medical modalities.

本研究提出了一种用于肿瘤检测和定位的高效注释策略，虽然比逐像素注释不够精确，但可以用于创建大规模的肿瘤筛查数据集。

在时间和体积数据中获取肿瘤定位的弱标注

Acquiring Weak Annotations for Tumor Localization in Temporal and  Volumetric Data

Recently proposed systems for open-domain question answering (OpenQA) require
large amounts of training data to achieve state-of-the-art performance.
However, data annotation is known to be time-consuming and therefore expensive
to acquire. As a result, the appropriate datasets are available only for a
handful of languages (mainly English and Chinese). In this work, we introduce
and publicly release PolQA, the first Polish dataset for OpenQA. It consists of
7,000 questions, 87,525 manually labeled evidence passages, and a corpus of
over 7,097,322 candidate passages. Each question is classified according to its
formulation, type, as well as entity type of the answer. This resource allows
us to evaluate the impact of different annotation choices on the performance of
the QA system and propose an efficient annotation strategy that increases the
passage retrieval performance by 10.55 p.p. while reducing the annotation cost
by 82%.

本文介绍并公开发布了 PolQA，这是第一个用于 OpenQA 的波兰语数据集。该数据集包含 7,000 个问题，87,525 个手动标注的证据段落和超过 7,097,322 个候选段落。该资源允许我们评估不同注释选择对 QA 系统性能的影响，并提出一种有效的注释策略，使召回率提高了 10.55 p.p. 并同时将注释成本降低了 82％。

通过手动注释提高问答性能：成本、效益和策略

Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies

A central challenge for the task of semantic segmentation is the prohibitive
cost of obtaining dense pixel-level annotations to supervise model training. In
this work, we show that in order to achieve a good level of segmentation
performance, all you need are a few well-chosen pixel labels. We make the
following contributions: (i) We investigate the novel semantic segmentation
setting in which labels are supplied only at sparse pixel locations, and show
that deep neural networks can use a handful of such labels to good effect; (ii)
We demonstrate how to exploit this phenomena within an active learning
framework, termed PixelPick, to radically reduce labelling cost, and propose an
efficient "mouse-free" annotation strategy to implement our approach; (iii) We
conduct extensive experiments to study the influence of annotation diversity
under a fixed budget, model pretraining, model capacity and the sampling
mechanism for picking pixels in this low annotation regime; (iv) We provide
comparisons to the existing state of the art in semantic segmentation with
active learning, and demonstrate comparable performance with up to two orders
of magnitude fewer pixel annotations on the CamVid, Cityscapes and PASCAL VOC
2012 benchmarks; (v) Finally, we evaluate the efficiency of our annotation
pipeline and its sensitivity to annotator error to demonstrate its
practicality.

通过仅在稀疏像素位置提供标签，深度神经网络可以实现良好的语义分割性能，结合主动学习机制和高效注释策略可以大大减少标注成本，并在 CamVid，Cityscapes 和 PASCAL VOC 2012 数据集上进行了实验证明了相当可比的性能。