The study evaluates and compares GPT-4 and GPT-4Vision for radiological
tasks, suggesting GPT-4Vision may recognize radiological features from images,
thereby enhancing its diagnostic potential over text-based descriptions.

评估和比较 GPT-4 和 GPT-4Vision 在放射学任务中的作用，表明 GPT-4Vision 可以从图像中识别放射学特征，从而提高其在诊断潜力上，超过基于文本的描述。

从文本到图像：探索 GPT-4Vision 在各个亚专业的先进放射学分析中的潜力

From Text to Image: Exploring GPT-4Vision's Potential in Advanced  Radiological Analysis across Subspecialties

Most uses of Meta-Learning in visual recognition are very often applied to
image classification, with a relative lack of works in other tasks {such} as
segmentation and detection. We propose a generic Meta-Learning framework for
few-shot weakly-supervised segmentation in medical imaging domains. We conduct
a comparative analysis of meta-learners from distinct paradigms adapted to
few-shot image segmentation in different sparsely annotated radiological tasks.
The imaging modalities include 2D chest, mammographic and dental X-rays, as
well as 2D slices of volumetric tomography and resonance images. Our
experiments consider a total of 9 meta-learners, 4 backbones and multiple
target organ segmentation tasks. We explore small-data scenarios in radiology
with varying weak annotation styles and densities. Our analysis shows that
metric-based meta-learning approaches achieve better segmentation results in
tasks with smaller domain shifts in comparison to the meta-training datasets,
while some gradient- and fusion-based meta-learners are more generalizable to
larger domain shifts.

本文提出了一个基于元学习的弱监督分割方案，并在医学成像领域进行了实验和对不同范例的比较分析，研究结果表明度量学习元学习方法在小型领域变化的任务中具有更好的分割结果，而某些梯度和融合的元学习方法则对大型领域变化具有更好的推广性。