Inspecting Model Fairness in Ultrasound Segmentation Tasks

With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications. While advancements in diagnostic precision are notable, some researchers have identified a concerning trend: their models exhibit biased performance across subgroups characterized by different sensitive attributes. This bias not only infringes upon the rights of patients but also has the potential to lead to life-altering consequences. In this paper, we inspect a series of DL segmentation models using two ultrasound datasets, aiming to assess the presence of model unfairness in these specific tasks. Our findings reveal that even state-of-the-art DL algorithms demonstrate unfair behavior in ultrasound segmentation tasks. These results serve as a crucial warning, underscoring the necessity for careful model evaluation before their deployment in real-world scenarios. Such assessments are imperative to ensure ethical considerations and mitigate the risk of adverse impacts on patient outcomes.Comment: Submitted to ISBI 202

Magnetic γ-Fe2O3-Loaded Attapulgite Sorbent for Hg0 Removal in Coal-Fired Flue Gas

A magnetically recoverable composite mercury removal sorbent was produced by introducing magnetic γ-Fe2O3 into attapulgite (ATT) (xFe1ATT) via the co-precipitation method and used to remove Hg0 in the simulated coal-fired power plant flue gas. The as-prepared 0.5Fe1ATT sorbent was characterized by X-ray diffraction, Brunauer–Emmett–Teller, transmission electron microscopy, vibrating sample magnetometer, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy analyses. The results showed that the Hg0 removal performance of the composite of γ-Fe2O3 and ATT was significantly promoted in comparison to pure γ-Fe2O3 and ATT individually. A relatively high magnetization value and good Hg0 removal performance were obtained by the sample of 0.5Fe1ATT. O2 could enhance Hg0 removal activity via the Mars–Maessen mechanism. NO displayed a significant promotion effect on Hg0 removal as a result of the formation of active species, such as NO2 and NO+. SO2 inhibited the removal of Hg0 as a result of its competition adsorption against Hg0 for the active sites and the sulfation of the sorbent. However, the introduction of NO could obviously alleviate the adverse effect of SO2 on the Hg0 removal capability. H2O showed a prohibitive effect on Hg0 removal as a result of its competition with Hg0 for the active sites. The findings of this study are of fundamental importance to the development of efficient and economic magnetic mercury sorbents for Hg0 removal from coal-fired boiler flue gases

Unsupervised Saliency Estimation based on Robust Hypotheses

Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than the smoothness term based on region dissimilarity when the image has complicated background or low contrast. The primal-dual interior point method is applied to optimize the proposed QP in polynomial time. Extensive experiments demonstrate that the proposed method can outperform 10 state-of-the-art methods on three public benchmark datasets