Automatic aorta segmentation from 3-D medical volumes is an important yet
difficult task. Several factors make the problem challenging, e.g. the
possibility of aortic dissection or the difficulty with segmenting and
annotating the small branches. This work presents a contribution by the MedGIFT
team to the SEG.A challenge organized during the MICCAI 2023 conference. We
propose a fully automated algorithm based on deep encoder-decoder architecture.
The main assumption behind our work is that data preprocessing and augmentation
are much more important than the deep architecture, especially in low data
regimes. Therefore, the solution is based on a variant of traditional
convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for
all testing cases with the highest stability among all participants. The method
scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative
results, and volumetric meshing quality, respectively. We freely release the
source code, pretrained model, and provide access to the algorithm on the
Grand-Challenge platform.

自动分割三维医学图像中的主动脉是一项重要且困难的任务，本研究提出了一种基于深度编码 - 解码架构的全自动算法，通过数据预处理和增强来解决低数据量情况下的主动脉分割问题，并在测试中获得了较高的稳定性和 Dice 分数。

使用经过大量增强的高分辨率三维 ResUNet 进行自动主动脉分割：对 SEG.A 挑战的贡献

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D  ResUNet: Contribution to the SEG.A Challenge

Automatic speech recognition (ASR) is a key technology in many services and
applications. This typically requires user devices to send their speech data to
the cloud for ASR decoding. As the speech signal carries a lot of information
about the speaker, this raises serious privacy concerns. As a solution, an
encoder may reside on each user device which performs local computations to
anonymize the representation. In this paper, we focus on the protection of
speaker identity and study the extent to which users can be recognized based on
the encoded representation of their speech as obtained by a deep
encoder-decoder architecture trained for ASR. Through speaker identification
and verification experiments on the Librispeech corpus with open and closed
sets of speakers, we show that the representations obtained from a standard
architecture still carry a lot of information about speaker identity. We then
propose to use adversarial training to learn representations that perform well
in ASR while hiding speaker identity. Our results demonstrate that adversarial
training dramatically reduces the closed-set classification accuracy, but this
does not translate into increased open-set verification error hence into
increased protection of the speaker identity in practice. We suggest several
possible reasons behind this negative result.

本文旨在研究自动语音识别中隐私保护的问题，提出了利用深度编码器 - 解码器架构中的预训练技术来保护说话人身份，通过对开放和封闭说话人集的说话人识别和验证实验，论证了对抗训练架构可以显著减少封闭集的分类精度，提高个人隐私保护的效果。