On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging

CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification

Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders the fair comparison of various facies classification approaches. To this end, we develop an objective benchmark for the evaluation of semi-supervised methods, including self-training, consistency regularization, and the proposed CONSS. Our benchmark is publicly available to enable researchers to objectively compare different approaches. Experimental results demonstrate that our approach achieves state-of-the-art performance on the F3 survey

MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents' communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05 degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size.Comment: Early accepted by MICCAI 202

Determination of 14 Elements in the Body Fluid and Hair of Lung Cancer Patients by Microwave Digestion with ICP-MS

Background and objective The trace element contents in the body fluid and hair are the important monitoring indicators for many diseases. The analysis of the trace element contents in the samples of lung cancer patients is helpful to the early diagnosis and treatment effectiveness evaluation to the patients. The aim of this study is to develop an ICPMS method for the determination of Cr, Fe, Mn, Al, Cd, Cu, Zn, Ni, Se, Pb, Ca, Mg, Sr, P in the body fluid and hair of lung cancer patients. Methods Samples of body fluid and hair from lung cancer patients were digested with microwave and 14 trace elements were determined with inductively coupled plasma mass spectrometry. Results GBW09101 standard reference material of human hair was used to validate the accuracy of the established method, and the results indicated that there is no obvious difference between the measured values and the references values. Forty-eight samples from 16 lung cancer patients were analyzed with the established method, and several generalizations were discovered. Conclusion The established method can be used for the multielement simultaneous determination of the samples of lung cancer patients, which are helpful to the diagnosis and treatment of the lung cancer

Research on Crowdsourcing Camera Mode System based on Block Chain Technology

As a new business model, the crowdsourcing camera mode has been paid close attention in recent years. However, traditional crowdsourcing operations rely too much on centralized institutions, which have many drawbacks. It is precisely because of this that the superiority of block chain technology is reflected. With the flourishing development of the Internet and the ever-changing block chain technology, this paper provides the research on crowdsourcing camera mode based on block chain technology from four aspects including the analysis of decentralization, the strengthen of security precautions, the establishment of appropriate systems and the gradually transition of token system

Research on Crowdsourcing Camera Mode System based on Block Chain Technology

As a new business model, the crowdsourcing camera mode has been paid close attention in recent years. However, traditional crowdsourcing operations rely too much on centralized institutions, which have many drawbacks. It is precisely because of this that the superiority of block chain technology is reflected. With the flourishing development of the Internet and the ever-changing block chain technology, this paper provides the research on crowdsourcing camera mode based on block chain technology from four aspects including the analysis of decentralization, the strengthen of security precautions, the establishment of appropriate systems and the gradually transition of token system

Determination of Vanadium, Iron, Aluminum and Phosphorus in Stone Coal Vanadium Ore by ICP-OES with Open Acid Dissolution

BACKGROUND： The exploration, research and utilization of stone coal vanadium ore resources need accurate composition determination, while there is no standard method for the determination of its main components such as vanadium, iron, aluminum and phosphorus. At the same time, every analysis method currently used has its own shortcomings. When the sample of stone coal vanadium ore is treated by alkali fusion and determined by ICP-OES, the high concentration of soluble salt will lead to high background and interference with the determination. The acid dissolution method can avoid the above problems, but when the conventional four acid system composed of HF+HCl+HNO3+HClO4 is used to digest the sample, the components to be tested in the sample cannot be completely released, so the sample needs to be burned at high temperature to remove carbon, making the process is cumbersome. OBJECTIVES： In order to establish a method for the determination of the main components in stone coal vanadium ore samples by ICP-OES using five acid systems to digest samples. METHODS: A mixed acid system of H2SO4+HF+HCl+HNO3+HClO4 with electric hot plate heating and hydrochloric acid extraction were used to digest the samples. The contents of vanadium, iron, aluminum and phosphorus in stone coal vanadium were determined by ICP-OES. RESULTS: Using the strong oxidizing property of sulfuric acid to oxidize a large amount of carbon in the sample into carbon dioxide, eliminated the process of burning carbon removal, eliminated the adsorption and wrapping of carbon-containing substances on the sample, and significantly enhanced the digestion effect. When the sample weight was 0.1g and the concentration of sulfuric acid was 0.30mL, the digestion rate of the sample reached more than 99%. The detection limit was 17-51mg/kg, relative standard deviation (n=11) was between 1.7%-5.1%, and relative error was -4.6%-2.7%. CONCLUSIONS: The method has the advantages of low background, high efficiency and accuracy, and can meet the detection requirements of stone coal and vanadium ore samples

CONSS: Contrastive Learning Method for Semisupervised Seismic Facies Classification

Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries. In addition, the annotation is a highly time-consuming task, especially when dealing with 3-D seismic data volumes. While traditional semisupervised methods reduce dependence on annotation, they are susceptible to interference from unreliable pseudolabels. To address these challenges, we propose a semisupervised seismic facies classification method called CONSS, which effectively mitigates classification confusion through contrastive learning. Our proposed method requires only 1% of labeled data, significantly reducing the demand for annotation. To minimize the influence of unreliable pseudolabels, we also introduce a confidence strategy to select positive and negative sample pairs from reliable regions for contrastive learning. Experimental results on the publicly available seismic datasets, the Netherlands F3 and SEAM AI challenge datasets, demonstrate that the proposed method outperforms classic semisupervised methods, including self-training and consistency regularization, achieving exceptional classification performance