Many skin lesion analysis (SLA) methods recently focused on developing a
multi-modal-based multi-label classification method due to two factors. The
first is multi-modal data, i.e., clinical and dermoscopy images, which can
provide complementary information to obtain more accurate results than
single-modal data. The second one is that multi-label classification, i.e.,
seven-point checklist (SPC) criteria as an auxiliary classification task can
not only boost the diagnostic accuracy of melanoma in the deep learning (DL)
pipeline but also provide more useful functions to the clinical doctor as it is
commonly used in clinical dermatologist's diagnosis. However, most methods only
focus on designing a better module for multi-modal data fusion; few methods
explore utilizing the label correlation between SPC and skin disease for
performance improvement. This study fills the gap that introduces a Graph
Convolution Network (GCN) to exploit prior co-occurrence between each category
as a correlation matrix into the DL model for the multi-label classification.
However, directly applying GCN degraded the performances in our experiments; we
attribute this to the weak generalization ability of GCN in the scenario of
insufficient statistical samples of medical data. We tackle this issue by
proposing a Graph-Ensemble Learning Model (GELN) that views the prediction from
GCN as complementary information of the predictions from the fusion model and
adaptively fuses them by a weighted averaging scheme, which can utilize the
valuable information from GCN while avoiding its negative influences as much as
possible. To evaluate our method, we conduct experiments on public datasets.
The results illustrate that our GELN can consistently improve the
classification performance on different datasets and that the proposed method
can achieve state-of-the-art performance in SPC and diagnosis classification.

本文提出了一种基于图卷积网络和集成学习的方法，用于皮肤病变分析中的多模态数据的多标签分类，该方法可以成功地利用先前的共现关系，可在不同数据集上提高分类性能并取得最新成果。

基于皮肤镜和临床图像的多标签皮损分类图集学习模型

Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification  using Dermoscopy and Clinical Images

Multiagent ensemble learning is an important class of algorithms aimed at
creating accurate and robust machine learning models by combining predictions
from individual agents. A key challenge for the design of these models is to
create effective rules to combine individual predictions for any particular
input sample.
This paper addresses this challenge and proposes a unique integration of
constrained optimization and learning to derive specialized consensus rules to
compose accurate predictions from a pretrained ensemble. The resulting
strategy, called end-to-end Multiagent ensemble Learning (e2e-MEL), learns to
select appropriate predictors to combine for a particular input sample. The
paper shows how to derive the ensemble learning task into a differentiable
selection program which is trained end-to-end within the ensemble learning
model. Results over standard benchmarks demonstrate the ability of e2e-MEL to
substantially outperform conventional consensus rules in a variety of settings.

本文提出了一种名为 e2e-MEL 的策略，通过将约束优化和学习相结合，将集成学习任务变成可微分的选择程序，学习为特定输入样本选择适当的预测器进行组合，结果表明，e2e-MEL 在各种设置中都可以明显优于传统共识规则。

多智体合奏的端到端优化与学习

End-to-End Optimization and Learning for Multiagent Ensembles

In recent years, we witnessed great progress in different tasks of natural
language understanding using machine learning. Question answering is one of
these tasks which is used by search engines and social media platforms for
improved user experience. Arabic is the language of the Holy Qur'an; the sacred
text for 1.8 billion people across the world. Arabic is a challenging language
for Natural Language Processing (NLP) due to its complex structures. In this
article, we describe our attempts at OSACT5 Qur'an QA 2022 Shared Task, which
is a question answering challenge on the Holy Qur'an in Arabic. We propose an
ensemble learning model based on Arabic variants of BERT models. In addition,
we perform post-processing to enhance the model predictions. Our system
achieves a Partial Reciprocal Rank (pRR) score of 56.6% on the official test
set.

本文描述了作者们在 OSACT5 古兰经问答大赛中的尝试，基于 BERT 模型的阿拉伯语变体，我们提出了一种集成学习模型，并进行了后处理来增强模型预测。我们的系统在官方测试集上达到了 56.6％的部分倒数排名（pRR）分数。