We study thorax disease classification in this paper.

Precise image segmentation provides clinical study with instructive information.

While optimization-based methods boast gen-eralizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature.

Here, we characterize the radiology report as a distinct domain and introduce RaLEs, the Radiology Language Evaluations, as a benchmark for natural language understanding and generation in radiology.

These models excel in zero-shot and few-shot learning, enabling them to learn new tasks without parameter updates.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations.