Clinical conversation summarization has become an important application of
Natural language Processing. In this work, we intend to analyze summarization
model ensembling approaches, that can be utilized to improve the overall
accuracy of the generated medical report called chart note. The work starts
with a single summarization model creating the baseline. Then leads to an
ensemble of summarization models trained on a separate section of the chart
note. This leads to the final approach of passing the generated results to
another summarization model in a multi-layer/stage fashion for better coherency
of the generated text. Our results indicate that although an ensemble of models
specialized in each section produces better results, the multi-layer/stage
approach does not improve accuracy. The code for the above paper is available
at this https URL

本研究旨在分析总结模型集合的方法，以提高医疗报告的总体准确性，虽然每个部分专门使用总结模型的总体准确性更高，但多层 / 阶段方法并不能提高准确性。

IUTEAM1 在 MEDIQA-Chat 2023 中：简易调整是否对临床对话的多层摘要有效？

IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for  multilayer summarization of clinical conversations?

Radiology report generation aims at generating descriptive text from
radiology images automatically, which may present an opportunity to improve
radiology reporting and interpretation. A typical setting consists of training
encoder-decoder models on image-report pairs with a cross entropy loss, which
struggles to generate informative sentences for clinical diagnoses since normal
findings dominate the datasets. To tackle this challenge and encourage more
clinically-accurate text outputs, we propose a novel weakly supervised
contrastive loss for medical report generation. Experimental results
demonstrate that our method benefits from contrasting target reports with
incorrect but semantically-close ones. It outperforms previous work on both
clinical correctness and text generation metrics for two public benchmarks.

本篇论文提出了一种弱监督的对比损失方法，用于提高放射学报告生成的文本质量，实验结果表明该方法在两个公共基准数据集上在临床正确性和文本生成指标方面优于以前的工作。

胸部 X 光报告生成的弱监督对比学习

Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation

Recently a number of studies demonstrated impressive performance on diverse
vision-language multi-modal tasks such as image captioning and visual question
answering by extending the BERT architecture with multi-modal pre-training
objectives. In this work we explore a broad set of multi-modal representation
learning tasks in the medical domain, specifically using radiology images and
the unstructured report. We propose Medical Vision Language Learner (MedViLL),
which adopts a BERT-based architecture combined with a novel multi-modal
attention masking scheme to maximize generalization performance for both
vision-language understanding tasks (diagnosis classification, medical
image-report retrieval, medical visual question answering) and vision-language
generation task (radiology report generation). By statistically and rigorously
evaluating the proposed model on four downstream tasks with three radiographic
image-report datasets (MIMIC-CXR, Open-I, and VQA-RAD), we empirically
demonstrate the superior downstream task performance of MedViLL against various
baselines, including task-specific architectures. The source code is publicly
available at: this https URL

本文提出了一种名为 MedViLL 的多模态自然语言处理模型，基于 BERT，使用一种新颖的多模态注意力掩码机制，通过在医学领域内的广泛一系列的多模态表现学习任务，包括影像报告分类、影像报告检索、影像问题回答和放射学报告生成，进行了统计和严格的评估，证明了 MedViLL 在各种基线上的优越性能表现，尤其是在三个影像报告数据集（MIMIC-CXR、Open-I 和 VQA-RAD）上的实现。