Supervised models trained to predict properties from representations have
been achieving high accuracy on a variety of tasks. For instance, the BERT
family seems to work exceptionally well on the downstream task from NER tagging
to the range of other linguistic tasks. But the vocabulary used in the medical
field contains a lot of different tokens used only in the medical industry such
as the name of different diseases, devices, organisms, medicines, etc. that
makes it difficult for traditional BERT model to create contextualized
embedding. In this paper, we are going to illustrate the System for Named
Entity Tagging based on Bio-Bert. Experimental results show that our model
gives substantial improvements over the baseline and stood the fourth runner up
in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1
score behind the best one.

通过使用 Bio-Bert 模型进行实体标记，我们的模型在医疗领域的数据集上取得显著的性能提升。

针对实验室协议的领域特定 BERT 表示进行命名实体识别

Domain specific BERT representation for Named Entity Recognition of lab  protocol

Recent works have shown that deep neural networks can achieve super-human
performance in a wide range of image classification tasks in the medical
imaging domain. However, these works have primarily focused on classification
accuracy, ignoring the important role of uncertainty quantification.
Empirically, neural networks are often miscalibrated and overconfident in their
predictions. This miscalibration could be problematic in any automatic
decision-making system, but we focus on the medical field in which neural
network miscalibration has the potential to lead to significant treatment
errors. We propose a novel calibration approach that maintains the overall
classification accuracy while significantly improving model calibration. The
proposed approach is based on expected calibration error, which is a common
metric for quantifying miscalibration. Our approach can be easily integrated
into any classification task as an auxiliary loss term, thus not requiring an
explicit training round for calibration. We show that our approach reduces
calibration error significantly across various architectures and datasets.

本文提出了一种新的校准方法，旨在提高神经网络在医学领域图像分类任务中的不确定性量化能力，该方法基于期望校准误差作为量化神经网络不确定性的公共度量，可以作为辅助损失项轻松地集成到任何分类任务中，从而显著降低分类误差。

医学成像分类神经网络的改进可训练校准方法

Improved Trainable Calibration Method for Neural Networks on Medical  Imaging Classification

Shortage of labeled data has been holding the surge of deep learning in
healthcare back, as sample sizes are often small, patient information cannot be
shared openly, and multi-center collaborative studies are a burden to set up.
Distributed machine learning methods promise to mitigate these problems. We
argue for a split learning based approach and apply this distributed learning
method for the first time in the medical field to compare performance against
(1) centrally hosted and (2) non collaborative configurations for a range of
participants. Two medical deep learning tasks are used to compare split
learning to conventional single and multi center approaches: a binary
classification problem of a data set of 9000 fundus photos, and multi-label
classification problem of a data set of 156,535 chest X-rays. The several
distributed learning setups are compared for a range of 1-50 distributed
participants. Performance of the split learning configuration remained constant
for any number of clients compared to a single center study, showing a marked
difference compared to the non collaborative configuration after 2 clients (p <
0.001) for both sets. Our results affirm the benefits of collaborative training
of deep neural networks in health care. Our work proves the significant benefit
of distributed learning in healthcare, and paves the way for future real-world
implementations.

本研究采用基于分离学习的分布式学习方法，首次将其应用于医学领域，并使用 9000 张眼底照片的二元分类问题和 156,535 张胸部 X 射线的多标记分类问题进行比较。结果表明，相对于单一中心研究和非协作配置，分离学习配置具有更好的性能，这证实了在卫生保健领域中采用深度神经网络的协作培训的益处，并为未来实际应用铺平了道路。