Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks

Melanoma is a dangerous form of skin cancer caused by the abnormal growth of skin cells. Fully Convolutional Network (FCN) approaches, including the U-Net architecture, can automatically segment skin lesions to aid diagnosis. The symmetrical U-Net model has shown outstanding results, but its use of a convolutional operation limits its ability to capture long-range dependencies, which are essential for accurate medical image segmentation. In addition, the U-shaped structure suffers from the semantic gaps between the encoder and decoder. In this study, we developed and evaluated a U-shaped hierarchical Transformer-based structure for skin lesion segmentation while we proposed an Inter-scale Context Fusion (ISCF) to utilize the attention correlations in each stage of the encoder to adaptively combine the contexts coming from each stage to hinder the semantic gaps. The preliminary results of the skin lesion segmentation benchmark endorse the applicability and efficacy of the ISCF module

Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models. Trained on large-scale dataset to bridge the gap between different modalities, foundation models facilitate contextual reasoning, generalization, and prompt capabilities at test time. The predictions of these models can be adjusted for new tasks by augmenting the model input with task-specific hints called prompts without requiring extensive labeled data and retraining. Capitalizing on the advances in computer vision, medical imaging has also marked a growing interest in these models. To assist researchers in navigating this direction, this survey intends to provide a comprehensive overview of foundation models in the domain of medical imaging. Specifically, we initiate our exploration by providing an exposition of the fundamental concepts forming the basis of foundation models. Subsequently, we offer a methodical taxonomy of foundation models within the medical domain, proposing a classification system primarily structured around training strategies, while also incorporating additional facets such as application domains, imaging modalities, specific organs of interest, and the algorithms integral to these models. Furthermore, we emphasize the practical use case of some selected approaches and then discuss the opportunities, applications, and future directions of these large-scale pre-trained models, for analyzing medical images. In the same vein, we address the prevailing challenges and research pathways associated with foundational models in medical imaging. These encompass the areas of interpretability, data management, computational requirements, and the nuanced issue of contextual comprehension.Comment: The paper is currently in the process of being prepared for submission to MI

Using a novel algorithm in ultrasound images to detect renal stones

Medical ultrasound is utilized as the primary method for the detection of kidney stones. Ultrasound imaging is often more popular than other imaging techniques because it is portable, low-cost, non-invasive, and does not utilize ionizing radiations. In this paper, three essential segmentation algorithms, namely Fuzzy C-means, K-means, and Expectation–Maximization algorithms, are proposed for the identification of renal stone in kidney ultrasound images. Expectation–Maximization algorithm is a novel method used by us for the first time for identifying renal stones. Initially, ultrasound kidney image is pre-processed. The pre-processing of ultrasound images comprises of denoising utilizing wavelet thresholding technique. The pre-processed image is taken as input for the segmentation process. Fuzzy C-means, K-means, and Expectation–Maximization algorithms are used to segment the renal calculi from the kidney ultrasound image; further region parameters are extracted from the segmented region. According to our results, K-means algorithm has the average accuracy, precision, and sensitivity equal to 99.82%, 92.83%, and 48.44%, respectively, and the average computation time is 4.31 s. As for the Fuzzy C-means algorithm, we report those values: 99.87, 80.59, 53.17%, and the average computation time is 346.29 s. Finally, for the proposed Expectation–Maximization algorithm, the values are 99.96, 82.38, and 84.52%, with the average computation time equal to 58.02 s. Fuzzy C-means produce better results than K-means segmentation, but it requires more computation time than K-means segmentation. Our proposed method has much better results than the other two methods and can find the renal stones in less than a minute