To improve the recognition ability of computer-aided breast mass
classification among mammographic images, in this work we explore the
state-of-the-art classification networks to develop an ensemble mechanism.
First, the regions of interest (ROIs) are obtained from the original dataset,
and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are
trained individually. After training, we ensemble the mechanism by summing the
probabilities outputted from each network which enhances the performance up to
5%. The scheme has been validated on a public dataset and we achieved accuracy,
precision, and recall 88%, 85%, and 76% respectively.

本研究探索了三种分类网络，即 XceptionNet、DenseNet 和 EfficientNet，通过集成机制来提高计算机辅助乳腺肿块分类的识别能力。在公共数据集上验证该方案，可以获得 88％的准确度，85％的精度和 76％的召回率。

乳腺肿瘤分类的集成卷积神经网络

Ensemble CNNs for Breast Tumor Classification

Acronym identification focuses on finding the acronyms and the phrases that
have been abbreviated, which is crucial for scientific document understanding
tasks. However, the limited size of manually annotated datasets hinders further
improvement for the problem. Recent breakthroughs of language models
pre-trained on large corpora clearly show that unsupervised pre-training can
vastly improve the performance of downstream tasks. In this paper, we present
an Adversarial Training BERT method named AT-BERT, our winning solution to
acronym identification task for Scientific Document Understanding (SDU)
Challenge of AAAI 2021. Specifically, the pre-trained BERT is adopted to
capture better semantic representation. Then we incorporate the FGM adversarial
training strategy into the fine-tuning of BERT, which makes the model more
robust and generalized. Furthermore, an ensemble mechanism is devised to
involve the representations learned from multiple BERT variants. Assembling all
these components together, the experimental results on the SciAI dataset show
that our proposed approach outperforms all other competitive state-of-the-art
methods.

本文提出了一个名为 AT-BERT 的对抗训练 BERT 方法，它采用预训练 BERT 进行更好的语义表示，结合 FGM 对抗性训练策略进行微调，采用多个 BERT 变体的表示，显著提高了 SDU 挑战赛的首字母缩略词识别任务的性能。