Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.

Photothermal diffusion-wave imaging is a promising technique for non-destructive evaluation and medical applications. Several diffusion-wave techniques have been developed to produce depth-resolved planar images of solids and to overcome imaging depth and image blurring limitations imposed by the physics of parabolic diffusion waves. Truncated-Correlation Photothermal Coherence Tomography (TC-PCT) is the most successful class of these methodologies to-date providing 3-D subsurface visualization with maximum depth penetration and high axial and lateral resolution. To extend the depth range and axial and lateral resolution, an in-depth analysis of TC-PCT, a novel imaging system with improved instrumentation, and an optimized reconstruction algorithm over the original TC-PCT technique is developed. Thermal waves produced by a laser chirped pulsed heat source in a finite thickness solid and the image reconstruction algorithm are investigated from the theoretical point of view. 3-D visualization of subsurface defects utilizing the new TC-PCT system is reported. The results demonstrate that this method is able to detect subsurface defects at the depth range of ∼4 mm in a steel sample, which exhibits dynamic range improvement by a factor of 2.6 compared to the original TC-PCT. This depth does not represent the upper limit of the enhanced TC-PCT. Lateral resolution in the steel sample was measured to be ∼31 μm.

Introduction: Detecting plant stress is a critical challenge in agriculture, where early intervention is essential to enhance crop resilience and maximize yield. Conventional single-mode approaches often fail to capture the complex interplay of plant health stressors.Methods: This review integrates findings from recent advancements in Multi-Mode Analytics (MMA), which employs spectral imaging, image-based phenotyping, and adaptive computational techniques. It integrates machine learning, data fusion, and hyperspectral technologies to improve analytical accuracy and efficiency.Results: MMA approaches have shown substantial improvements in the accuracy and reliability of early interventions. They outperform traditional methods by effectively capturing complex interactions among various abiotic stressors. Recent research highlights the benefits of MMA in enhancing predictive capabilities, which facilitates the development of timely and effective intervention strategies to boost agricultural productivity.Discussion: The advantages of MMA over conventional single-mode techniques are significant, particularly in the detection and management of plant stress in challenging environments. Integrating advanced analytical methods supports precision agriculture by enabling proactive responses to stress conditions. These innovations are pivotal for enhancing food security in terrestrial and space agriculture, ensuring sustainability and resilience in food production systems.

The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.

This article discusses a number of dynamic thermography techniques used for analysis of captured raw infrared images of objects. The most widely used, as well as state-of-the-art, modalities are outlined in terms of their operating principles and their specifications. Special attention is paid to truncated correlation photothermal coherence tomography, a novel method that provides high contrast and high resolution 3D images of objects. Applications of thermography in medical imaging, art analysis, and non-destructive imaging of industrial materials are discussed.

Early cancer non-invasive diagnosis is a leading medical topic worldwide due to the threat to human life and the high death rate of this disease. Light-absorption-based thermophotonic diagnostic imaging is well positioned for this challenge thanks to its speed, safety, and high molecular contrast advantages. In this Letter, an enhanced truncated-correlation photothermal coherence tomography (TC-PCT) imaging modality is presented for early in vivo tumor detection and tested using a nude mouse thigh. Compared with conventional TC-PCT, the enhanced imaging modality was found to exhibit higher contrast that contributed to the precise measurement of the size and shape of the detected tumor. The experimental results were validated following histological analysis from hematoxylin and eosin staining. This increased contrast advantage gives rise to possible clinical applications in early tumor detection and treatment and in monitoring the effects of anti-tumor drugs.