During the diagnostic process, doctors incorporate multimodal information
including imaging and the medical history - and similarly medical AI
development has increasingly become multimodal. In this paper we tackle a more
subtle challenge: doctors take a targeted medical history to obtain only the
most pertinent pieces of information; how do we enable AI to do the same? We
develop a wrapper method named MINT (Make your model INTeractive) that
automatically determines what pieces of information are most valuable at each
step, and ask for only the most useful information. We demonstrate the efficacy
of MINT wrapping a skin disease prediction model, where multiple images and a
set of optional answers to $25$ standard metadata questions (i.e., structured
medical history) are used by a multi-modal deep network to provide a
differential diagnosis. We show that MINT can identify whether metadata inputs
are needed and if so, which question to ask next. We also demonstrate that when
collecting multiple images, MINT can identify if an additional image would be
beneficial, and if so, which type of image to capture. We showed that MINT
reduces the number of metadata and image inputs needed by 82% and 36.2%
respectively, while maintaining predictive performance. Using real-world AI
dermatology system data, we show that needing fewer inputs can retain users
that may otherwise fail to complete the system submission and drop off without
a diagnosis. Qualitative examples show MINT can closely mimic the step-by-step
decision making process of a clinical workflow and how this is different for
straight forward cases versus more difficult, ambiguous cases. Finally we
demonstrate how MINT is robust to different underlying multi-model classifiers
and can be easily adapted to user requirements without significant model
re-training.

开发了一种名为 MINT（模型交互）的包装器方法，能够自动确定在每个步骤中最有价值的信息，为皮肤病预测模型提供多个图像和一组可选的元数据问题，证明了 MINT 在减少信息输入、保持预测性能以及模拟临床工作流程决策过程方面的效果。

MINT: 使多模态和多图像 AI 模型互动的封装器

MINT: A wrapper to make multi-modal and multi-image AI models  interactive

We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372
tweets in 10 languages including English, French, Spanish, Italian, Portuguese,
Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular
multilingual pre-trained language models. The dataset is released along with
the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
(this https URL).

提出了一个名为 MINT 的针对 13,372 条推文的多语言亲密性分析数据集，并在英语、法语、西班牙语、意大利语、葡萄牙语、韩语、荷兰语、中文、印地语和阿拉伯语等 10 种语言上进行了基准测试，并发布了该数据集。同时，与此数据集相关的语义分析比赛任务为 SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis。

SemEval 2023 任务 9：多语言推文亲密度分析

SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis

We propose a test of independence of two multivariate random vectors, given a
sample from the underlying population. Our approach, which we call MINT, is
based on the estimation of mutual information, whose decomposition into joint
and marginal entropies facilitates the use of recently-developed efficient
entropy estimators derived from nearest neighbour distances. The proposed
critical values, which may be obtained from simulation (in the case where one
marginal is known) or resampling, guarantee that the test has nominal size, and
we provide local power analyses, uniformly over classes of densities whose
mutual information satisfies a lower bound. Our ideas may be extended to
provide a new goodness-of-fit tests of normal linear models based on assessing
the independence of our vector of covariates and an appropriately-defined
notion of an error vector. The theory is supported by numerical studies on both
simulated and real data.

提出了一种称为 MINT 的新方法，该方法基于互信息的估计，并使用从最近的邻居距离导出的有效熵估计器，从而便于将互信息分解为联合和边际熵，用于检验两个多变量随机向量的独立性，并可将其扩展为基于测量协变量向量与误差向量独立性的新正态线性模型是否适配的卡方检验，并在模拟和真实数据上进行了数值研究。