Machine learning for non-invasive tissue characterization in body imaging

Tissue characterization plays a vital role in the diagnosis of various diseases, as it involves the analysis of the structural, biochemical, and physiological properties of tissues to differentiate healthy tissue from tissue with pathological abnormalities [1]. The current gold standard for tissue characterization is histopathological examination, which involves obtaining a tissue sample through a relatively invasive procedure. Due to this invasiveness, obtaining tissue for histopathological examination can be a painful procedure and carries the risk of hemorrhage.The aim of this thesis is to determine the role of artificial intelligence (AI) techniques for tissue characterization on medical imaging. More specifically, correlation will be made between the results of medical imaging analysis by AI techniques and the results of histopathological examination, with the ultimate goal to substitute relatively invasive biopsy or surgical procedures needed for histopathological examinations with non-invasive AI-based methods applied on medical imaging

CLE Diffusion: Controllable Light Enhancement Diffusion Model

Low light enhancement has gained increasing importance with the rapid development of visual creation and editing. However, most existing enhancement algorithms are designed to homogeneously increase the brightness of images to a pre-defined extent, limiting the user experience. To address this issue, we propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion, a novel diffusion framework to provide users with rich controllability. Built with a conditional diffusion model, we introduce an illumination embedding to let users control their desired brightness level. Additionally, we incorporate the Segment-Anything Model (SAM) to enable user-friendly region controllability, where users can click on objects to specify the regions they wish to enhance. Extensive experiments demonstrate that CLE Diffusion achieves competitive performance regarding quantitative metrics, qualitative results, and versatile controllability. Project page: \url{https://yuyangyin.github.io/CLEDiffusion/

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning

Liver fibrosis staging by deep learning:a visual-based explanation of diagnostic decisions of the model

OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage

Optimal Bus-Bridging Service under a Metro Station Disruption

A station disruption is an abnormal operational situation that the entrance or exit gates of a metro station have to be closed for a certain of time due to an unexpected incident. The passengers’ travel behavioral responses to the alternative station disruption scenarios and the corresponding controlling strategies are complex and hard to capture. This can lead to the hardness of estimating the changes of the network-wide passenger demand, which is the basis of carrying out a response plan. This paper will establish a model to solve the metro station disruption problem by providing optimal additional bus-bridging services. Two main contributions are made: "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mo stretchy="false""("/mml:mo""mml:mn fontstyle="italic""1"/mml:mn""mml:mo stretchy="false"")"/mml:mo""/mml:math" a three-layer discrete choice behavior model is developed to analyze the dynamic passenger flow demand under station disruption; and "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"""mml:mo stretchy="false""("/mml:mo""mml:mn fontstyle="italic""2"/mml:mn""mml:mo stretchy="false"")"/mml:mo""/mml:math" an integrated algorithm is designed to manage and control the station disruption crisis by providing additional bus-bridging services with the objective of minimizing the total travel time of affected passengers and the operating cost of bridging-buses. Besides, the multimodal transport modes, including metro, bridging-bus, shared-bike, and taxi, are considered as passengers’ alternative choices in face of the station disruption. A numerical study based on the Beijing metro network shows that additional bus-bridging services can significantly eliminate the negative impact of the station disruption. Document type: Articl

Doublecortin-Like Kinase 1 (DCLK1) Regulates B Cell-Specific Moloney Murine Leukemia Virus Insertion Site 1 (Bmi-1) and is Associated with Metastasis and Prognosis in Pancreatic Cancer

Background/Aims: Cancer stem cells (CSCs) are largely responsible for tumor relapse and metastatic behavior. Doublecortin-like kinase 1 (DCLK1) was recently reported to be a biomarker for gastrointestinal CSCs and involved in the epithelial-mesenchymal transition (EMT) and tumor progression. B cell-specific Moloney murine leukemia virus insertion site 1 (Bmi-1) is a crucial regulator of CSC self-renewal, malignant transformation and EMT, and a previous study from our group showed that Bmi-1 is upregulated in pancreatic cancer progression and participates in EMT. However, it remains unclear whether DCLK1 is involved in pancreatic cancer or whether DCLK1 is associated with the altered level of Bmi-1 expression. Methods: The correlation of DCLK1 expression and clinical features of pancreatic cancer was analyzed in 210 paraffin-embedded archived pancreatic cancer specimens by immunohistochemical analysis. The biological effects of DCLK1 siRNA on cells were investigated by examining cell proliferation using a cell counting kit and cell colony assays, cell migration by wound healing assay and cell invasion by Transwell invasion assay. We further investigated the effect of therapeutic siRNA targeting DCLK1 on pancreatic cancer cell growth in vivo. Moreover, the molecular mechanism by which DCLK1 upregulates Bmi-1 expression was explored using real-time PCR, western blotting and Co-immunoprecipitation assay. Results: DCLK1 is overexpressed in pancreatic cancer and is related to metastasis and prognosis. Knockdown of DCLK1 markedly suppressed cell growth in vitro and in vivo and also inhibited the migration and invasion of pancreatic cancer cells. Furthermore, we found that DCLK1 silencing could inhibit EMT in cancer cells via downregulation of Bmi-1 and the mesenchymal markers Snail and Vimentin and upregulation of the epithelial marker E-cadherin. Moreover, high DCLK1 expression in human pancreatic cancer samples was associated with a mesenchymal phenotype and increased cell proliferation. Further co-immunoprecipitation indicated that DCLK1 did not interact with Bmi-1 directly. Conclusion: Our data suggest that upregulation of DCLK1 may contribute to pancreatic cancer metastasis and poor prognosis by increasing Bmi-1 expression indirectly. The findings indicate that inhibiting DCLK1 expression might be a novel strategy for pancreatic cancer therapy

Overall and cause-specific mortality rates among men and women with high exposure to indoor air pollution from the use of smoky and smokeless coal: a cohort study in Xuanwei, China

OBJECTIVES: Never-smoking women in Xuanwei (XW), China, have some of the highest lung cancer rates in the country. This has been attributed to the combustion of smoky coal used for indoor cooking and heating. The aim of this study was to evaluate the spectrum of cause-specific mortality in this unique population, including among those who use smokeless coal, considered 'cleaner' coal in XW, as this has not been well-characterised. DESIGN: Cohort study. SETTING: XW, a rural region of China where residents routinely burn coal for indoor cooking and heating. PARTICIPANTS: Age-adjusted, cause-specific mortality rates between 1976 and 2011 were calculated and compared among lifetime smoky and smokeless coal users in a cohort of 42 420 men and women from XW. Mortality rates for XW women were compared with those for a cohort of predominately never-smoking women in Shanghai. RESULTS: Mortality in smoky coal users was driven by cancer (41%), with lung cancer accounting for 88% of cancer deaths. In contrast, cardiovascular disease (CVD) accounted for 32% of deaths among smokeless coal users, with 7% of deaths from cancer. Total cancer mortality was four times higher among smoky coal users relative to smokeless coal users, particularly for lung cancer (standardised rate ratio (SRR)=17.6). Smokeless coal users had higher mortality rates of CVD (SRR=2.9) and pneumonia (SRR=2.5) compared with smoky coal users. These patterns were similar in men and women, even though XW women rarely smoked cigarettes. Women in XW, regardless of coal type used, had over a threefold higher rate of overall mortality, and most cause-specific outcomes were elevated compared with women in Shanghai. CONCLUSIONS: Cause-specific mortality burden differs in XW based on the lifetime use of different coal types. These observations provide evidence that eliminating all coal use for indoor cooking and heating is an important next step in improving public health particularly in developing countries

Automatic Labeling of X-Ray Images Based on Deep Learning

Coronary artery disease is the most common type of heart disease, which influences 110 million people's health and causes 8.9 million deaths in 2015. Physicians can visualize the lesion in coronary arteries by cardiac angiography (X-ray image) during diagnosis and treatment of coronary artery disease. The pathological findings in cardiac angiography are reported per segment or per artery of the coronary artery tree, therefore, it requires to annotate the name of each segment or artery in the coronary artery tree. This thesis proposes a data-driven method as a first attempt at annotating cardiac angiography based on deep learning. The method aims at automatically regressing segment points between different segments on the coronary artery tree as the annotation of the cardiac angiography. The proposed data-driven cardiac angiography annotation methods can learn and generalize from manually annotated cardiac angiography examples, but its performance is limited by the number and quality of examples for learning.Computer Scienc

Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter

Acoustic feedback is a common phenomenon that occurs during hearing aid use, limiting the maximum gain that a hearing aid can provide. Effective cancellation of acoustic feedback is an essential feature of hearing aids. However, due to the complex environments in which hearing aids are used and the frequently changing acoustic feedback path, it is difficult for existing adaptive filter-based acoustic feedback cancellation algorithms to balance both convergence speed and steady-state error. For this reason, based on the nonparametric variable step size (NPVSS) algorithm, a weighted NPVSS algorithm that also introduces a prediction error method is proposed in this paper. First, by introducing the prediction error method, the adaptive filter bias caused by the nonwhite source signal is effectively reduced. Second, the proposed weighting mechanism weights the error signal according to the adaptive filter misalignment, which enhances the steady-state robustness of the algorithm while accelerating its convergence. In addition, a new low-complexity method is herein proposed for source signal energy estimation by reusing the misalignment information to solve the step size calculation problem of the NPVSS algorithm. Simulation results show that the new algorithm exhibits greater robustness and faster convergence than similar algorithms. The proposed algorithm is implemented with a real hearing aid and its performance is measured on a dummy head in a soundproof room. The test results demonstrate that the proposed algorithm achieves a 35% reduction in convergence time compared with PEM-IMLMS and a 60% reduction compared with PEM-NLMS. Furthermore, the proposed algorithm reduces the sound pressure level of acoustic feedback residues compared with PEM-IMLMS and PEM-NLMS by approximately 2 dB SPL and 6 dB SPL, respectively. These results indicate that the new algorithm can provide timely and stable cancellation of acoustic feedback