Abstract
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

Abstract
Single Page Applications (SPAs) are characterised by changing a webpage's content without forcing a reload. While they increase the interactivity of web pages, they can also raise accessibility concerns, such as new content being displayed without screen reader users being aware. Another concern raised impacts the ability of current web accessibility evaluation tools to be able to assess SPAs. Current tools work by loading the DOM and assessing it. For SPAs, which change the DOM in response to user interaction, it is possible that evaluation tools do not have access to a significant part of a page's content. In this paper, we introduce QualState, a tool that can browse the different states of a SPA, and provide the DOM of the different states to QualWeb, an automated web accessibility evaluation tool, so their accessibility can be evaluated. We assessed QualState in a small set of SPAs and concluded that it increases the number of elements evaluated and improves QualWeb's ability to identify accessibility barriers.

Abstract
Owing to their similarities with giant exoplanets, brown dwarf companions of stars provide insights into the fundamental processes of planet formation and evolution. From their orbits, several brown dwarf companions are found to be more massive than theoretical predictions given their luminosities and the ages of their host stars1-3. Either the theory is incomplete or these objects are not single entities. For example, they could be two brown dwarfs each with a lower mass and intrinsic luminosity1,4. The most problematic example is Gliese 229 B (refs. 5,6), which is at least 2-6 times less luminous than model predictions given its dynamical mass of 71.4 +/- 0.6 Jupiter masses (MJup) (ref. 1). We observed Gliese 229 B with the GRAVITY interferometer and, separately, the CRIRES+ spectrograph at the Very Large Telescope. Both sets of observations independently resolve Gliese 229 B into two components, Gliese 229 Ba and Bb, settling the conflict between theory and observations. The two objects have a flux ratio of 0.47 +/- 0.03 at a wavelength of 2 mu m and masses of 38.1 +/- 1.0 and 34.4 +/- 1.5 MJup, respectively. They orbit each other every 12.1 days with a semimajor axis of 0.042 astronomical units (au). The discovery of Gliese 229 BaBb, each only a few times more massive than the most massive planets, and separated by 16 times the Earth-moon distance, raises new questions about the formation and prevalence of tight binary brown dwarfs around stars. Analysis of the cool brown dwarf Gliese 229 B suggests that it is actually a close binary of two less massive brown dwarfs, explaining its low luminosity and settling the conflict between theoretical predictions and measurements.

Abstract
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, underscoring the need for accurate and reliable diagnostic tools. While AI- driven models have shown significant promise in identifying CAD through imaging techniques, their 'black box' nature often hinders clinical adoption due to a lack of interpretability. In response, this paper proposes a novel approach to image captioning specifically tailored for CAD diagnosis, aimed at enhancing the transparency and usability of AI systems. Utilizing the COCA dataset, which comprises gated coronary CT images along with Ground Truth (GT) segmentation annotations, we introduce a hybrid model architecture that combines a Vision Transformer (ViT) for feature extraction with a Generative Pretrained Transformer (GPT) for generating clinically relevant textual descriptions. This work builds on a previously developed 3D Convolutional Neural Network (CNN) for coronary artery segmentation, leveraging its accurate delineations of calcified regions as critical inputs to the captioning process. By incorporating these segmentation outputs, our approach not only focuses on accurately identifying and describing calcified regions within the coronary arteries but also ensures that the generated captions are clinically meaningful and reflective of key diagnostic features such as location, severity, and artery involvement. This methodology provides medical practitioners with clear, context-rich explanations of AI-generated findings, thereby bridging the gap between advanced AI technologies and practical clinical applications. Furthermore, our work underscores the critical role of Explainable AI (XAI) in fostering trust, improving decision- making, and enhancing the efficacy of AI-driven diagnostics, paving the way for future advancements in the field.

Abstract

Abstract
Surface plasmon resonance (SPR) conventionally occurs at the interface of a thin metallic film and an external dielectric medium in fiber optics through core-guided light. However, this work introduces theoretical and experimental evidence suggesting that the SPR in optical fibers can also be induced through light scattering from Au nanoparticles (NPs) on the thin metallic film, defined as nanoparticle-induced SPR (NPI-SPR). This method adheres to phase-matching conditions between SPR dispersion curves and the wave vectors of scattered light from Au NPs. Experimentally, these conditions are met on an etched optical fiber, enabling direct interaction between light and immobilized Au NPs. Compared to SPR, NPI-SPR exhibits stronger field intensity in the external region and wavelength tuning capabilities (750 to 1250 nm) by varying Au NP diameters (20 to 90 nm). NPI-SPR demonstrates refractive index sensitivities of 4000 to 4416 nm per refractive index unit, nearly double those of typical SPR using the same optical fiber configuration sans Au NPs. Additionally, NPI-SPR fiber configuration has demonstrated its applicability for developing biosensors, achieving a remarkable limit of detection of 0.004 nm for thrombin protein evaluation, a twenty-fold enhancement compared to typical SPR. These findings underscore the intrinsic advantages of NPI-SPR for sensing. Surface plasmon resonance (SPR) typically occurs at the interface of a thin metallic film and a dielectric medium in fiber optics. This work presents evidence of nanoparticle-induced SPR (NPI-SPR) in optical fibers through light scattering from Au nanoparticles on the thin metallic film. NPI-SPR offers stronger field intensity, wavelength tuning, and enhanced refractive index sensitivities, making it advantageous for biosensing applications. image