Depression is the most prevalent and serious mental illness, which induces
grave financial and societal ramifications. Depression detection is key for
early intervention to mitigate those consequences. Such a high-stake decision
inherently necessitates interpretability, which most existing methods fall
short of. To connect human expertise in this decision-making, safeguard trust
from end users, and ensure algorithm transparency, we develop an interpretable
deep learning model: Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet
is built upon the emergent prototype learning methods. In line with the medical
practice of depression diagnosis, MSTPNet differs from existing prototype
learning models in its capability of capturing the depressive symptoms and
their temporal distribution such as frequency and persistence of appearance.
Extensive empirical analyses using real-world social media data show that
MSTPNet outperforms state-of-the-art benchmarks in depression detection, with
an F1-score of 0.851. Moreover, MSTPNet interprets its prediction by
identifying what depression symptoms the user presents and how long these
related symptoms last. We further conduct a user study to demonstrate its
superiority over the benchmarks in interpretability. Methodologically, this
study contributes to extant literature with a novel interpretable deep learning
model for depression detection in social media. Our proposed method can be
implemented in social media platforms to detect depression and its symptoms.
Platforms can subsequently provide personalized online resources such as
educational and supporting videos and articles, or sources for treatments and
social support for depressed patients.

本研究提出了一种基于 MSTPNet 的解释性深度学习模型，通过对社交媒体数据进行广泛的实证分析，该模型在抑郁症检测方面优于现有模型，还能够确定抑郁症症状及其持续时间，并通过用户研究证明了对现有模型在可解释性方面的优越性。

社交媒体中抑郁症检测的可解释人工智能方法：症状及持续时间如何？

What Symptoms and How Long? An Interpretable AI Approach for Depression  Detection in Social Media

In many medical subfields, there is a call for greater interpretability in
the machine learning systems used for clinical work. In this paper, we design
an interpretable deep learning model to predict the presence of 6 types of
brainwave patterns (Seizure, LPD, GPD, LRDA, GRDA, other) commonly encountered
in ICU EEG monitoring. Each prediction is accompanied by a high-quality
explanation delivered with the assistance of a specialized user interface. This
novel model architecture learns a set of prototypical examples (``prototypes'')
and makes decisions by comparing a new EEG segment to these prototypes. These
prototypes are either single-class (affiliated with only one class) or
dual-class (affiliated with two classes).
We present three main ways of interpreting the model: 1) Using
global-structure preserving methods, we map the 1275-dimensional cEEG latent
features to a 2D space to visualize the ictal-interictal-injury continuum and
gain insight into its high-dimensional structure. 2) Predictions are made using
case-based reasoning, inherently providing explanations of the form ``this EEG
looks like that EEG.'' 3) We map the model decisions to a 2D space, allowing a
user to see how the current sample prediction compares to the distribution of
predictions made by the model.
Our model performs better than the corresponding uninterpretable (black box)
model with $p<0.01$ for discriminatory performance metrics AUROC (area under
the receiver operating characteristic curve) and AUPRC (area under the
precision-recall curve), as well as for task-specific interpretability metrics.
We provide videos of the user interface exploring the 2D embedded space,
providing the first global overview of the structure of ictal-interictal-injury
continuum brainwave patterns. Our interpretable model and specialized user
interface can act as a reference for practitioners who work with cEEG patterns.

设计了一种可解释的深度学习模型来预测 ICU 脑电监测中常见的 6 种脑波模式（癫痫，LPD，GPD，LRDA，GRDA，其他），并提供专业的用户界面，从而在全局 - 结构保持方法的帮助下，可视化 ICTIL-INTERICTAL-INJURY 连续性脑波模式的 2D 嵌入式空间以及通过基于案例推理的方式提供解释。

一种可解释的机器学习系统，用于识别癫痫发作 - 间歇期 - 损伤连续体上的脑电图模式

An Interpretable Machine Learning System to Identify EEG Patterns on the Ictal-Interictal-Injury Continuum

Quantification of anatomical shape changes currently relies on scalar global
indexes which are largely insensitive to regional or asymmetric modifications.
Accurate assessment of pathology-driven anatomical remodeling is a crucial step
for the diagnosis and treatment of many conditions. Deep learning approaches
have recently achieved wide success in the analysis of medical images, but they
lack interpretability in the feature extraction and decision processes. In this
work, we propose a new interpretable deep learning model for shape analysis. In
particular, we exploit deep generative networks to model a population of
anatomical segmentations through a hierarchy of conditional latent variables.
At the highest level of this hierarchy, a two-dimensional latent space is
simultaneously optimised to discriminate distinct clinical conditions, enabling
the direct visualisation of the classification space. Moreover, the anatomical
variability encoded by this discriminative latent space can be visualised in
the segmentation space thanks to the generative properties of the model, making
the classification task transparent. This approach yielded high accuracy in the
categorisation of healthy and remodelled left ventricles when tested on unseen
segmentations from our own multi-centre dataset as well as in an external
validation set, and on hippocampi from healthy controls and patients with
Alzheimer's disease when tested on ADNI data. More importantly, it enabled the
visualisation in three-dimensions of both global and regional anatomical
features which better discriminate between the conditions under exam. The
proposed approach scales effectively to large populations, facilitating
high-throughput analysis of normal anatomy and pathology in large-scale studies
of volumetric imaging.

通过深度生成网络模拟人体解剖分割的总体和局部形貌特征，并利用条件潜在变量实现对临床疾病的可解释结果可视化诊断，从而为大规模的体积成像研究提供高通量分析的新方法。