Assessing relative risks of municipal wastewater disposal options for Southeast Florida.

A comparative assessment of the risks of the three current wastewater effluent disposal options and three other potential options was conducted for Southeast Florida communities. The question was how the risk to humans from the use of potable reuse compares to the other five available wastewater disposal alternatives. The need for this type of risk assessment is due to the potential to use potable reuse as a water supply and the potential resistance from the public as a result of such a proposal.

Thyroid Ultrasound Image Database and Marker Mask Inpainting Method for Research and Development.

Objective: The main objective of this study was to build a rich and high-quality thyroid ultrasound image database (TUD) for computer-aided diagnosis (CAD) systems to support accurate diagnosis and prognostic modeling of thyroid disorders. Because most of the raw thyroid ultrasound images contain artificial markers, which seriously affect the robustness of CAD systems because of their strong prior location information, we propose a marker mask inpainting (MMI) method to erase artificial markers and improve image quality.

Methods: First, a set of thyroid ultrasound images were collected from the General Hospital of the Northern Theater Command.

RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images.

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking layers, leading to repetitive computational tasks and unnecessarily large neural networks.

RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs).