Recent advancements in blind image quality assessment (BIQA) are primarily propelled by deep learning technologies. While leveraging transformers can effectively capture long-range dependencies and contextual details in images, the significance of local information in image quality assessment can be undervalued. To address this challenging problem, we propose a novel feature enhancement framework tailored for BIQA. Specifically, we devise an Adaptive Graph Attention (AGA) module to simultaneously augment both local and contextual information. It not only refines the post-transformer features into an adaptive graph, facilitating local information enhancement, but also exploits interactions amongst diverse feature channels. The proposed technique can better reduce redundant information introduced during feature updates compared to traditional convolution layers, streamlining the self-updating process for feature maps. Experimental results show that our proposed model outperforms state-of-the-art BIQA models in predicting the perceived quality of images. The code of the model will be made publicly available.

Previous studies have shown that there is a strong correlation between radiologists' diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists' visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists' gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model can be achieved by pre-training the model on a large-scale natural image saliency dataset and then fine-tuning it on the target medical image dataset. In addition, based on a systematic selection of backbone networks and network architectures, we proposed a parallel multi-stream encoded model which outperforms the state-of-the-art approaches for predicting saliency of mammograms.

Radiologists’ eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement data can be modeled to represent clinically relevant regions in a medical image and potentially integrated into an artificial intelligence (AI) system for automatic diagnosis in medical imaging. In this article, we first conduct a large-scale eye-tracking study involving 13 radiologists interpreting 191 chest X-ray (CXR) images, establishing a best-of-its-kind CXR visual saliency benchmark. We then perform analysis to quantify the reliability and clinical relevance of saliency maps (SMs) generated for CXR images. We develop CXR image saliency prediction method (CXRSalNet), a novel saliency prediction model that leverages radiologists’ gaze information to optimize the use of unlabeled CXR images, enhancing training and mitigating data scarcity. We also demonstrate the application of our CXR saliency model in enhancing the performance of AI-powered diagnostic imaging systems.

No-reference image quality assessment (NR-IQA) aims to quantitatively measure human perception of visual quality without comparing a distorted image to a reference. Despite recent advances, existing NR-IQR approaches often demonstrate insufficient ability to capture perceptual cues in the absence of a reference, limiting their generalisability across diverse and complex real-world image degradations. These limitations hinder their ability to match the reliability of full-reference IQA (FR-IQA) counterparts. A key challenge, therefore, is to enable NR-IQA models to emulate the reference-aware reasoning exhibited by humans and FR-IQA methods. To address this challenge, we propose a novel NR-IQA model based on a knowledge-sharing (KS) strategy to simulate this capability and predict image quality more effectively. Specifically, we designate an FR-IQA model as the teacher and an NR-IQA model as the student. Unlike conventional knowledge distillation (KD), our proposed architecture enables the NR-IQA student and FR-IQA teacher to share a decoder rather than being independent models. Furthermore, the student model contains a Mental Imagery Generation (MIG) module to learn mental imagery as the reference. To fully exploit local and global information, we adopt a vision transformer (ViT) branch and a convolutional neural network branch for feature extraction (FE). Finally, a quality-aware regressor (QAR) combined with deep ordinal regression is constructed to infer the quality score. Experiments show that our proposed NR-IQA model, KSIQA, has class-leading performance against current no-reference (NR) techniques across widespread benchmark datasets.

When images undergo quality degradation caused by editing, compression or transmission, their saliency tends to shift away from its original position. Saliency shifts indicate visual behaviour change and therefore contain vital information regarding perception of visual content and its distortions. Given a pristine image and its distorted format, we want to be able to detect saliency shifts induced by distortions. The resulting saliency shift map (SSM) can be used to identify the region and degree of visual distraction caused by distortions, and consequently to perceptually optimise image coding or enhancement algorithms. To this end, we first create a largest-of-its-kind eye-tracking database, comprising 60 pristine images and their associated 540 distorted formats viewed by 96 subjects. We then propose a computational model to predict the saliency shift map (SSM), utilising transformers and convolutional neural networks. Experimental results demonstrate that the proposed model is highly effective in detecting distortion-induced saliency shifts in natural images.