Medical image reporting focused on automatically generating the diagnostic reports from medical images has garnered growing research attention. In this task, learning cross-modal alignment between images and reports is crucial. However, the exposure bias problem in autoregressive text generation poses a notable challenge, as the model is optimized by a word-level loss function using the teacher-forcing strategy. To this end, we propose a novel Token-Mixer framework that learns to bind image and text in one embedding space for medical image reporting. Concretely, Token-Mixer enhances the cross-modal alignment by matching image-to-text generation with text-to-text generation that suffers less from exposure bias. The framework contains an image encoder, a text encoder and a text decoder. In training, images and paired reports are first encoded into image tokens and text tokens, and these tokens are randomly mixed to form the mixed tokens. Then, the text decoder accepts image tokens, text tokens or mixed tokens as prompt tokens and conducts text generation for network optimization. Furthermore, we introduce a tailored text decoder and an alternative training strategy that well integrate with our Token-Mixer framework. Extensive experiments across three publicly available datasets demonstrate Token-Mixer successfully enhances the image-text alignment and thereby attains a state-of-the-art performance. Related codes are available at https://github.com/yangyan22/Token-Mixer.

The potential of automated radiology report generation in alleviating the time-consuming tasks of radiologists is increasingly being recognized in medical practice. Existing report generation methods have evolved from using image-level features to the latest approach of utilizing anatomical regions, significantly enhancing interpretability. However, directly and simplistically using region features for report generation compromises the capability of relation reasoning and overlooks the common attributes potentially shared across regions. To address these limitations, we propose a novel region-based Attribute Prototype-guided Iterative Scene Graph generation framework (AP-ISG) for report generation, utilizing scene graph generation as an auxiliary task to further enhance interpretability and relational reasoning capability. The core components of AP-ISG are the Iterative Scene Graph Generation (ISGG) module and the Attribute Prototype-guided Learning (APL) module. Specifically, ISSG employs an autoregressive scheme for structural edge reasoning and a contextualization mechanism for relational reasoning. APL enhances intra-prototype matching and reduces inter-prototype semantic overlap in the visual space to fully model the potential attribute commonalities among regions. Extensive experiments on the MIMIC-CXR with Chest ImaGenome datasets demonstrate the superiority of AP-ISG across multiple metrics.

Medical report generation generates the corresponding report according to the given radiology image, which has been attracting increasing research interest. However, existing methods mainly adopt supervised training which rely on large amount of medical reports that are actually unavailable owing to the labor-intensive labeling process and privacy protection protocol. In the meanwhile, the intrinsic relationships between local pathological changes in the image are often ignored, which actually are important hints to high quality report generation. To this end, we propose a Relation-Aware Mean Teacher (RAMT) framework, which follows a standard mean teacher paradigm for semi-supervised report generation. The key to the encoder of the backbone network is the Graph-guided Hybrid Feature Encoding (GHFE) module, which exploits a prior disease knowledge graph to encode the intrinsic relations between pathological changes into the graph embedding and learns a word dictionary to retrieve the semantic embedding for each potential pathological change. GHFE combines the graph embedding, semantic embedding and visual features to form hybrid features, which are sent to a Transformer-based decoder for report generation. Extensive experiments on the MIMIC-CXR and IU X-Ray datasets demonstrate the effectiveness of our proposed approach.

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multi-modal tasks.

Medical image report generation (MeIRG) aims at generating associated diagnosis descriptions with natural language sentences from medical images, which is essential in the computer-aided diagnosis system. Nevertheless, this task remains challenging in that medical images and linguistic expressions should be understood jointly which however show great discrepancies in the modality. To fill this visual-to-semantic gap, we propose a novel framework that follows the encoder-decoder pipeline. Our framework is characterized by encoding both deep visual and semantic embeddings through a triple-branch network (TriNet) during the encoding phase. The visual attention branch captures attended visual embeddings from medical images with the soft-attention mechanism. The medical report (MeRP) embedding branch predicts semantic report embeddings. The embedding branch of medical subject headings (MeSH) obtains semantic embeddings of related medical tags as complementary information. Then, outputs of these branches are fused and fed into a decoder for the report generation. Experimental results on two benchmark datasets have demonstrated the excellent performance of our method. Related codes are available athttps://github.com/yangyan22/Medical-Report-Generation-TriNet.