Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image quality of low-dose CT (LDCT) to reduce the cumulative radiation exposure of patients undergoing routine screening exams. The resurgence of deep learning has yielded a new approach for noise reduction by training a deep multi-layer convolutional neural networks (CNN) to map the low-dose to normaldose CT images. However, CNN-based methods heavily rely on convolutional kernels, which use fixed-size filters to process one local neighborhood within the receptive field at a time. As a result, they are not efficient at retrieving structural information across large regions. In this paper, we propose a novel 3D self-attention convolutional neural network for the LDCT denoising problem. Our 3D selfattention module leverages the 3D volume of CT images to capture a wide range of spatial information both within CT slices and between CT slices. With the help of the 3D self-attention module, CNNs are able to leverage pixels with stronger relationships regardless of their distance and achieve better denoising results. In addition, we propose a self-supervised learning scheme to train a domain-specific autoencoder as the perceptual loss function. We combine these two methods and demonstrate their effectiveness on both CNN-based neural networks and WGAN-based neural networks with comprehensive experiments. Tested on the AAPM-Mayo Clinic Low Dose CT Grand Challenge data set, our experiments demonstrate that self-attention (SA) module and autoencoder (AE) perceptual loss function can efficiently enhance traditional CNNs and can achieve comparable or better results than the state-of-the-art methods.

BACKGROUND: Given the costs of delivering care for men with prostate cancer remain poorly described, this article reports the results of time‐driven activity‐based costing (TDABC) for competing treatments of low‐risk prostate cancer.METHODS: Process maps were developed for each phase of care from the initial urologic visit through 12 years of follow‐up for robotic‐assisted laparoscopic prostatectomy (RALP), cryotherapy, high–dose rate (HDR) and low–dose rate (LDR) brachytherapy, intensity‐modulated radiation therapy (IMRT), stereotactic body radiation therapy (SBRT), and active surveillance (AS). The last modality incorporated both traditional transrectal ultrasound (TRUS) biopsy and multiparametric‐MRI/TRUS fusion biopsy. The costs of materials, equipment, personnel, and space were calculated per unit of time and based on the relative proportion of capacity used. TDABC for each treatment was defined as the sum of its resources.RESULTS: Substantial cost variation was observed at 5 years, with costs ranging from $7,298 for AS to $23,565 for IMRT, and they remained consistent through 12 years of follow‐up. LDR brachytherapy ($8,978) was notably cheaper than HDR brachytherapy ($11,448), and SBRT ($11,665) was notably cheaper than IMRT, with the cost savings attributable to shorter procedure times and fewer visits required for treatment. Both equipment costs and an inpatient stay ($2,306) contributed to the high cost of RALP ($16,946). Cryotherapy ($11,215) was more costly than LDR brachytherapy, largely because of increased single‐use equipment costs ($6,292 vs $1,921). AS reached cost equivalence with LDR brachytherapy after 7 years of follow‐up.CONCLUSIONS: The use of TDABC is feasible for analyzing cancer services and provides insights into cost‐reduction tactics in an era focused on emphasizing value. By detailing all steps from diagnosis and treatment through 12 years of follow‐up for low‐risk prostate cancer, this study has demonstrated significant cost variation between competing treatments. Cancer 2016;122:447–455. © 2015 American Cancer Society.

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.