Retrieval-augmented generation (RAG) is widely adopted for radiology report generation with medical vision-language models, leveraging external reports as linguistic references. However, existing RAG methods rely primarily on dense embedding similarity, which may retrieve reports that are semantically related yet clinically inconsistent with respect to presence or laterality constraints. Such inconsistencies are often propagated into generation, resulting in contradictory or unsupported findings. We propose an evidence-guided retrieval-augmented framework EviRAG that decomposes retrieval into structured and unstructured alignment levels. First, we induce structured clinical triplets from both query and database cases through targeted visual interrogation, projecting images into a shared evidence space. Triplet-level alignment enforces explicit agreement over presence and laterality variables, yielding a clinically admissible candidate set via structural ranking. Within this constrained space, we perform semantic alignment in a shared multimodal embedding space to capture nuanced descriptive correspondence. The top-ranked reports and query image are jointly fed into a medical vision-language model for report generation. Comprehensive experiments on radiology report generation benchmarks show that EviRAG substantially reduces clinical inconsistencies compared to strong medical vision-language baselines. The source code is available at https://github.com/liamgu06/EviRAG.

Due to the low storage cost and fast retrieval speed, multi-modal hashing, which maps the instances with different modal data-views into hash codes, has earned increasing research attention. Most existing supervised multi-modal hashing methods exploit the label information to define the similarities between instance pairs to supervise their training process. However, such methods ignore that the transitivity of their defined similarity has been broken in the multi-label scenarios, i.e., the instance$x$is similar to the instance$y$, and the instance$z$is also similar to the instance$y$, but$x$may be not similar to$z$, which will lead to fluctuations in the model optimization process and damage their retrieval performance. For example, in the first batch with instances$x$and$y$but without$z$, the model will be optimized to make the hash codes of$x$and$y$similar to each other; In the second batch with instances$z$and$y$but without$x$, the model will be optimized to make the hash codes of$z$and$y$similar to each other; In the third batch with the instances$x$and$z$but without$y$, the model will be optimized to make the hash codes of$z$and$x$dissimilar to each other, meanwhile in this process, the hash codes of$z$and$x$may be dissimilar to that of$y$which damage the optimizing results of the first two batches. Therefore, we propose a novel Similarity Transitivity Broken-aware Multi-modal Hashing, called STBMH, to solve this problem by adding a novel regularization loss into the original pair-wise loss. For each instance$x$in a training batch, the regularization loss will take all instances in the training set into account. Extensive experiments on four widely used datasets show that the proposed method achieves better performance than the state-of-the-art baselines on multi-modal retrieval task.

Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-modal hashing methods have attracted considerable attention from the researchers. For the supervised cross-modal hashing methods, how to make the learned hash codes sufficiently preserve semantic information contained in the label of datapoints is the key to further enhance the retrieval performance. Hence, almost all supervised cross-modal hashing methods usually depend on defining similarities between datapoints with the label information to guide the hashing model learning fully or partly. However, the defined similarity between datapoints can only capture the label information of datapoints partially and misses abundant semantic information, which then hinders the further improvement of retrieval performance. Thus, in this paper, different from previous works, we propose a novel cross-modal hashing method without defining the similarity between datapoints, called Deep Cross-modal Proxy Hashing (DCPH). Specifically, DCPH first trains a proxy hashing network to transform each category information of a dataset into a semantic discriminative hash code, called proxy hash code. Each proxy hash code can preserve the semantic information of its corresponding category well. Next, without defining the similarity between datapoints to supervise the training process of the modality-specific hashing networks, we propose a novelmargin-dynamic-softmax lossto directly utilize the proxy hashing codes as supervised information. Finally, by minimizing the novelmargin-dynamic-softmax loss, the modality-specific hashing networks can be trained to generate hash codes that can simultaneously preserve the cross-modal similarity and abundant semantic information well. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the cross-modal retrieval tasks.

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfactory performance by integrating feature learning and hash codes optimizing into a same framework. However, most existing deep cross-modal hashing methods either cannot learn a unified hash code for the two correlated data-points of different modalities in a database instance or cannot guide the learning of unified hash codes by the feedback of hashing function learning procedure, to enhance the retrieval accuracy. To address the issues above, in this paper, we propose a novel end-to-end Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning (DCHUC). Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs. With the iterative optimization algorithm, the learned unified hash codes can be used to guide the hashing function learning procedure; Meanwhile, the learned hashing functions can feedback to guide the unified hash codes optimizing procedure. Extensive experiments on three public datasets demonstrate that the proposed method outperforms the state-of-the-art cross-modal hashing methods.

Recently, deep supervised hashing methods have shown state-of-the-art performance by integrating feature learning and hash codes learning into an end-to-end network to generate high-quality hash codes. However, it is still a challenge to learn discriminative hash codes for preserving the label information of images efficiently. To overcome this difficulty, in this paper, we propose a novel Partial-Softmax Loss based Deep Hashing, called PSLDH, to generate high-quality hash codes. Specifically, PSLDH first trains a category hashing network to generate a discriminative hash code for each category, and the hash code will preserve semantic information of the corresponding category well. Then, instead of defining the similarity between datapairs using their corresponding label vectors, we directly use the learned hash codes of categories to supervise the learning process of image hashing network, and a novel Partial-SoftMax loss is proposed to optimize the image hashing network. By minimizing the novel Partial-SoftMax loss, the learned hash codes can preserve the label information of images sufficiently. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in image retrieval task.