Smart Home Healthcare Settings: A Qualitative Study of the Domain Boundary

Addressing the health problems of the 21st century will require individuals to use a new set of medical and public health resources that extend beyond historic and traditional medical devices and are built on current and smart information technologies. Much of these new medical tools was originally designed by device manufacturers to be used only in clinical settings and by trained healthcare professionals but recently are finding their way into the home nevertheless. Their migration to the home poses many challenges to both caregivers and care recipients. In order to facilitate their migration to the home, it is very important to first understand the domain boundary, its components and their interactions. Little research discusses the context of smart home healthcare and its surrounding entities to date. This paper aims to fill the knowledge gap by developing a framework of smart home healthcare context. To this end, we conducted semi-structured interviews with patients and health professionals served for or by home healthcare agencies on the east coast in the United States. We analyzed the content applying thematic approach. The findings revealed four major components of the framework including person, tasks, technologies, and environments. The findings also revealed us to define the interactions between these components. The findings have significant implications for smart home designers and manufacturers, and service providers

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

We propose a Generative Adversarial Network (GAN) optimized for noise reduction in CT-scans. The objective of CT scan denoising is to obtain higher quality imagery using a lower radiation exposure to the patient. Recent work in computer vision has shown that the use of Charbonnier distance as a term in the perceptual loss of a GAN can improve the performance of image reconstruction and video super-resolution. However, the use of a Charbonnier structural loss term has not yet been applied or evaluated for the purpose of CT scan denoising. Our proposed GAN makes use of a Wasserstein adversarial loss, a pretrained VGG19 perceptual loss, as well as a Charbonnier distance structural loss. We evaluate our approach using both applied Poisson noise distribution in order to simulate low-dose CT imagery, as well as using an anthropomorphic thoracic phantom at different exposure levels. Our evaluation criteria are Peek Signal to Noise (PSNR) as well as Structured Similarity (SSIM) of the denoised images, and we compare the results of our method versus recent state of the art deep denoising GANs. In addition, we report global noise through uniform soft tissue mediums. Our findings show that the incorporation of the Charbonnier Loss with the VGG-19 network improves the performance of the denoising as measured with the PSNR and SSIM, and that the method greatly reduces soft tissue noise to levels comparable to the NDCT scan