CADxReport: Chest x-ray report generation using co-attention mechanism and reinforcement learning

TL;DR: Researchers propose an auxiliary signal guidance and memory-driven network (ASGMD) to automatically generate radiology reports, addressing visual and textual data bias, and achieving state-of-the-art performance on two public datasets, IU-Xray and MIMIC-CXR, with released code.

TL;DR: Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other as discussed by the authors .

TL;DR: This review paper explores the advantages and challenges of standardized reporting, and presents a set of ten essential rules for creating standardized radiology reports, emphasizing clarity, consistency, and adherence to structured formats.

TL;DR: A novel Weakly Guided Attention Model with Hierarchical Interaction, named WGAM-HI, to improve Brain CT report generation is proposed, which conducts many-to-many matching for multiple visual images and semantic sentences via a hierarchical interaction framework with a two-layer attention model and aTwo-layer report generator.

TL;DR: In this article, an interleaved text/image deep learning system was proposed to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.

TL;DR: Structured reporting in radiology optimizes patient outcomes by standardizing report structures, terminology, and data elements, enabling automatic functions, data sharing, and research, requiring international collaboration and industry involvement for successful implementation.

TL;DR: It is argued that rib orientation can be used for rotation detection in chest radiographs as an aid in quality control during image acquisition and for training and testing data sets for computer aided diagnosis research.

TL;DR: This paper introduces an attention-based model for neural image caption generation, trained deterministically and stochastically, which learns to focus on salient objects while generating captions, achieving state-of-the-art performance on three benchmark datasets.