Lung cancer has been one of the major threats to human life for decades.
Computer-aided diagnosis can help with early lung nodul detection and
facilitate subsequent nodule characterization. Large Visual Language models
(VLMs) have been found effective for multiple downstream medical tasks that
rely on both imaging and text data. However, lesion level detection and
subsequent diagnosis using VLMs have not been explored yet. We propose CADe,
for segmenting lung nodules in a zero-shot manner using a variant of the
Segment Anything Model called MedSAM. CADe trains on a prompt suite on input
computed tomography (CT) scans by using the CLIP text encoder through prefix
tuning. We also propose, CADx, a method for the nodule characterization as
benign/malignant by making a gallery of radiomic features and aligning
image-feature pairs through contrastive learning. Training and validation of
CADe and CADx have been done using one of the largest publicly available
datasets, called LIDC. To check the generalization ability of the model, it is
also evaluated on a challenging dataset, LUNGx. Our experimental results show
that the proposed methods achieve a sensitivity of 0.86 compared to 0.76 that
of other fully supervised methods.The source code, datasets and pre-processed
data can be accessed using the link:

肺癌的计算机辅助诊断使用大型视觉语言模型，通过 CADe 和 CADx 方法实现肺结节的分割和良性 / 恶性的特征对齐，具有较高的敏感度（0.86）。

Lung-CADex：全自动胸部 CT 图像中肺结节的零样本检测和分类

Lung-CADex: Fully automatic Zero-Shot Detection and Classification of  Lung Nodules in Thoracic CT Images

Word2vec is one of the most used algorithms to generate word embeddings
because of a good mix of efficiency, quality of the generated representations
and cognitive grounding. However, word meaning is not static and depends on the
context in which words are used. Differences in word meaning that depends on
time, location, topic, and other factors, can be studied by analyzing
embeddings generated from different corpora in collections that are
representative of these factors. For example, language evolution can be studied
using a collection of news articles published in different time periods. In
this paper, we present a general framework to support cross-corpora language
studies with word embeddings, where embeddings generated from different corpora
can be compared to find correspondences and differences in meaning across the
corpora. CADE is the core component of our framework and solves the key problem
of aligning the embeddings generated from different corpora. In particular, we
focus on providing solid evidence about the effectiveness, generality, and
robustness of CADE. To this end, we conduct quantitative and qualitative
experiments in different domains, from temporal word embeddings to language
localization and topical analysis. The results of our experiments suggest that
CADE achieves state-of-the-art or superior performance on tasks where several
competing approaches are available, yet providing a general method that can be
used in a variety of domains. Finally, our experiments shed light on the
conditions under which the alignment is reliable, which substantially depends
on the degree of cross-corpora vocabulary overlap.

本文提出了一个通用框架，用于支持使用单词嵌入进行跨语料库的语言研究，其中可以比较从不同语料库生成的嵌入项，以找出语料库之间的含义对应和差异。该框架的核心组件是 CADE，重点解决了从不同语料库生成的嵌入项的对齐问题。经过实验，结果表明 CADE 在时间词嵌入，语言本地化和主题分析等各个领域都达到了最先进或更优异的性能。