Automating radiology report generation can significantly alleviate
radiologists' workloads. Previous research has primarily focused on realizing
highly concise observations while neglecting the precise attributes that
determine the severity of diseases (e.g., small pleural effusion). Since
incorrect attributes will lead to imprecise radiology reports, strengthening
the generation process with precise attribute modeling becomes necessary.
Additionally, the temporal information contained in the historical records,
which is crucial in evaluating a patient's current condition (e.g., heart size
is unchanged), has also been largely disregarded. To address these issues, we
propose RECAP, which generates precise and accurate radiology reports via
dynamic disease progression reasoning. Specifically, RECAP first predicts the
observations and progressions (i.e., spatiotemporal information) given two
consecutive radiographs. It then combines the historical records,
spatiotemporal information, and radiographs for report generation, where a
disease progression graph and dynamic progression reasoning mechanism are
devised to accurately select the attributes of each observation and
progression. Extensive experiments on two publicly available datasets
demonstrate the effectiveness of our model.

利用动态疾病进展推理生成精准准确的放射学报告，结合历史记录、时空信息和放射学图像，利用疾病进展图和动态进展推理机制，精确选择每个观察和进展的属性。

精确放射学报告生成的动态疾病进展推理

RECAP: Towards Precise Radiology Report Generation via Dynamic Disease  Progression Reasoning

We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and
effective audio captioning system that generates captions conditioned on an
input audio and other captions similar to the audio retrieved from a datastore.
Additionally, our proposed method can transfer to any domain without the need
for any additional fine-tuning. To generate a caption for an audio sample, we
leverage an audio-text model CLAP to retrieve captions similar to it from a
replaceable datastore, which are then used to construct a prompt. Next, we feed
this prompt to a GPT-2 decoder and introduce cross-attention layers between the
CLAP encoder and GPT-2 to condition the audio for caption generation.
Experiments on two benchmark datasets, Clotho and AudioCaps, show that RECAP
achieves competitive performance in in-domain settings and significant
improvements in out-of-domain settings. Additionally, due to its capability to
exploit a large text-captions-only datastore in a \textit{training-free}
fashion, RECAP shows unique capabilities of captioning novel audio events never
seen during training and compositional audios with multiple events. To promote
research in this space, we also release 150,000+ new weakly labeled captions
for AudioSet, AudioCaps, and Clotho.

RECAP 是一种新颖有效的音频字幕系统，通过从数据存储中检索与输入音频类似的其他字幕来生成字幕，并且无需额外的微调即可适用于任何领域。