Few-shot learning (FSL) presents immense potential in enhancing model
generalization and practicality for medical image classification with limited
training data; however, it still faces the challenge of severe overfitting in
classifier training due to distribution bias caused by the scarce training
samples. To address the issue, we propose MedMFG, a flexible and lightweight
plug-and-play method designed to generate sufficient class-distinctive features
from limited samples. Specifically, MedMFG first re-represents the limited
prototypes to assign higher weights for more important information features.
Then, the prototypes are variationally generated into abundant effective
features. Finally, the generated features and prototypes are together to train
a more generalized classifier. Experiments demonstrate that MedMFG outperforms
the previous state-of-the-art methods on cross-domain benchmarks involving the
transition from natural images to medical images, as well as medical images
with different lesions. Notably, our method achieves over 10% performance
improvement compared to several baselines. Fusion experiments further validate
the adaptability of MedMFG, as it seamlessly integrates into various backbones
and baselines, consistently yielding improvements of over 2.9% across all
results.

通过 MedMFG 方法，针对少样本学习在医学图像分类中的过拟合问题，生成丰富有效的特征，并结合原型训练出更具普适性的分类器，实验证明该方法在不同领域的图像分类上具有显著改进。

少样本医学图像分类的即插即用特征生成

Plug-and-Play Feature Generation for Few-Shot Medical Image  Classification

The UCR Time Series Archive - introduced in 2002, has become an important
resource in the time series data mining community, with at least one thousand
published papers making use of at least one data set from the archive. The
original incarnation of the archive had sixteen data sets but since that time,
it has gone through periodic expansions. The last expansion took place in the
summer of 2015 when the archive grew from 45 to 85 data sets. This paper
introduces and will focus on the new data expansion from 85 to 128 data sets.
Beyond expanding this valuable resource, this paper offers pragmatic advice to
anyone who may wish to evaluate a new algorithm on the archive. Finally, this
paper makes a novel and yet actionable claim: of the hundreds of papers that
show an improvement over the standard baseline (1-nearest neighbor
classification), a large fraction may be mis-attributing the reasons for their
improvement. Moreover, they may have been able to achieve the same improvement
with a much simpler modification, requiring just a single line of code.

UCR 时间序列档案在时间序列数据挖掘社区中已成为重要资源，提供了从 45 到 128 个数据集的扩展，并提供了实用的建议和观点。另外，该论文还指出，许多论文没有正确地归因于分类改善的原因，而只需一行代码即可实现相同的改善。