Recent advancements in pre-trained large foundation models (LFM) have yielded
significant breakthroughs across various domains, including natural language
processing and computer vision. These models have been particularly impactful
in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM
has been developed for fundus images using the Vision Transformer (VIT) and a
self-supervised learning framework. This LFM has shown promising performance in
fundus disease diagnosis across multiple datasets. On the other hand, deep
learning models have long been challenged by dataset quality issues, such as
image quality and dataset bias. To investigate the influence of data quality on
LFM, we conducted explorations in two fundus diagnosis tasks using datasets of
varying quality. Specifically, we explored the following questions: Is LFM more
robust to image quality? Is LFM affected by dataset bias? Can fine-tuning
techniques alleviate these effects? Our investigation found that LFM exhibits
greater resilience to dataset quality issues, including image quality and
dataset bias, compared to typical convolutional networks. Furthermore, we
discovered that overall fine-tuning is an effective adapter for LFM to mitigate
the impact of dataset quality issues.

使用预训练的大型基础模型（LFM）和自我监督学习框架为视网膜图像开发了一个 LFM，该模型在多个数据集上对视网膜疾病的诊断表现出良好的性能；此外，研究发现 LFM 对于数据集质量问题（包括图像质量和数据集偏差）表现出更强的韧性，而整体微调是减轻数据集质量问题影响的有效适应器。

大型基础模型在诊断中数据集质量依然令人关注吗？

Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation  Model?

Training speech translation (ST) models requires large and high-quality
datasets. MuST-C is one of the most widely used ST benchmark datasets. It
contains around 400 hours of speech-transcript-translation data for each of the
eight translation directions. This dataset passes several quality-control
filters during creation. However, we find that MuST-C still suffers from three
major quality issues: audio-text misalignment, inaccurate translation, and
unnecessary speaker's name. What are the impacts of these data quality issues
for model development and evaluation? In this paper, we propose an automatic
method to fix or filter the above quality issues, using English-German (En-De)
translation as an example. Our experiments show that ST models perform better
on clean test sets, and the rank of proposed models remains consistent across
different test sets. Besides, simply removing misaligned data points from the
training set does not lead to a better ST model.

本研究提出了一种自动的解决 MuST-C 语音翻译数据集中存在的质量问题的方法，使得标准测试集合的质量得到提高，模型发展的结果也得到了一致的认可。