Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated learning research applied to medical image analysis, outlining technical contributions. We followed the guidelines of Okali and Schabram, a review methodology, to produce a comprehensive summary and discussion of the literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, and Web of Science. We found a total of 433 papers and selected 118 of them for further examination. The findings highlighted research on applying federated learning to neural network methods in cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, and urology. The main challenges reported were the ability of machine learning models to adapt effectively to real-world datasets and privacy preservation. We outlined two strategies to address these challenges: non-independent and identically distributed data and privacy-enhancing methods. This review paper offers a reference overview for those already working in the field and an introduction to those new to the topic.

Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of $89.79\%$ and $90.35\%$, and an intersection over union (IoU) of $82.34\%$ and $83.21\%$ on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.