Definition of Substance and Non-substance Addiction.

Substance addiction (or drug addiction) is a neuropsychiatric disorder characterized by a recurring desire to continue taking the drug despite harmful consequences. Non-substance addiction (or behavioral addiction) covers pathological gambling, food addiction, internet addiction, and mobile phone addiction. Their definition is similar to drug addiction but they differ from each other in specific domains. This review aims to provide a brief overview of past and current definitions of substance and non-substance addiction, and also touches on the topic of diagnosing drug addiction and non-drug addiction, ultimately aiming to further the understanding of the key concepts needed for a foundation to study the biological and psychological underpinnings of addiction disorders.

Short-term effects of air pollution and weather changes on the occurrence of acute aortic dissection in a cold region.

Background: Air pollution and severe weather conditions can adversely affect cardiovascular disease emergencies. Nevertheless, it remains unclear whether air pollutants and low ambient temperature can trigger the occurrence of acute aortic dissection (AAD) in cold regions. Methods: We applied a retrospective analysis to assess the short-term effects of air pollution and ambient temperature on the occurrence of AAD in Harbin, China. A total of 564 AAD patients were enrolled from a major hospital in Harbin between January 1, 2017, and February 5, 2021. Weather condition data and air pollutant concentrations, including fine particulate matter smaller than 10 µm (PM10) and 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), were collected every day. Conditional logistic regressions and correlation analysis were applied to analyze the relationship of environmental and atmospheric parameters with AAD occurrence at lags of 0 to 7 days. Specifically, we appraised the air quality index, CO, NO2, SO2, O3, PM10, PM2.5, temperature, dew point temperature, atmospheric pressure, and cloud amount. Results: A total of 1,496 days at risk were assessed, of which 564 patients developed AAD. Specifically, AAD did not occur on 1,043 (69.72%) days, while 1 or more cases occurred on 453 (30.28%) days. Several pollution and weather predictors for AAD were confirmed by multilevel modeling. The air quality index (p = 0.0012), cloud amount (p = 0.0001), and concentrations of PM2.5 (p = 0.0004), PM10 (p = 0.0013), NO2 (p = 0.0007) and O3 (p = 0.0001) predicted AAD as early as 7 days before the incident (lag of 7 days) in the study period. However, only concentrations of the air pollutants NO2 (p = 0.0468) and O3 (p = 0.011) predicted the occurrence of AAD after the COVID-19 outbreak. Similar predictive effects were observed for temperature, dew point temperature, and atmospheric pressure (all p < 0.05) on all days. Conclusion: The risk of AAD is closely related to air pollution and weather characteristics in Harbin. While causation was not determined, the impact of air pollutants on the risk of AAD was reduced after the COVID-19 outbreak.

Breast ultrasound image classification based on multiple-instance learning.

Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).

A Novel Distant Domain Transfer Learning Framework for Thyroid Image Classification.

Medical ultrasound imaging technology is currently the preferred method for early diagnosis of thyroid nodules. Radiologists' analysis of ultrasound images is highly dependent on their clinical experience and is susceptible to intra- and inter-observer variability. Although end-to-end deep learning technique can address these limitations, the difficulty of acquiring annotated medical image makes it very challenging. Transfer learning can alleviate the problems, but the large gap between source and target domain will lead to negative transfer. In this paper, a novel transfer learning method with distant domain high-level feature fusion (DHFF) model is proposed. It reduces the distribution distance between the source domain and the target domain while maintaining the characteristics of respective domains, which can avoid excessive feature fusion while enabling the model to learn more valuable transfer knowledge. The DHFF is validated by multiple public source and private target datasets in experiments. The results show that the classification accuracy of DHFF is up to 88.92% with thyroid ultrasound auxiliary source domains, which is up to 8% higher than existing transfer and distant transfer algorithms.

EMT-NET: EFFICIENT MULTITASK NETWORK FOR COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER.

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.

BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.

Quantitative measurement for thyroid cancer characterization based on elastography.

OBJECTIVES: The purpose of this study was to evaluate color thyroid elastograms quantitatively and objectively and select more effective features to differentiate benign from malignant thyroid nodules. METHODS: The study was approved by the Ethics Committee of Harbin Medical University. A total of 125 cases (56 malignant and 69 benign) were analyzed in this retrospective study. The original color thyroid elastograms were transferred from the red-green-blue color space to the hue-saturation-value color space. The elasticity information was represented by the hue component of color elastograms. The lesion regions were delineated by radiologists, and statistical and textural features were extracted. Then the most effective and reliable features among them were selected by using a minimum redundancy-maximum relevance algorithm. The selected features were input to a support vector machine to differentiate benign from malignant thyroid nodules. RESULTS: The classification accuracy was 93.6% when the hard area ratio and textural feature (energy) of the lesion region were used. The area under the receiver operating characteristic curve for the hard area ratio was higher than that for the strain ratio (0.97 versus 0.87; P < .01), and the area under the curve for the hard area ratio was also higher than that for the color score (0.97 versus 0.80; P < .001). The results also showed that the features were robust for lesion region delineation. CONCLUSIONS: The hard area ratio is an important and quantitative metric for elastograms. Quantitative analysis of elastograms using computer-aided diagnostic techniques can improve diagnostic accuracy.

A Novel Adaptive Fuzzy Deep Learning Approach for Histopathologic Cancer Detection.

We proposed a novel model that integrates the fuzzy theory and group equivariant convolutional neural network for histopathologic cancer detection. The proposed fuzzy group equivariant convolutional neural network consists of the convolutional network, a novel fuzzy global pooling layer, and a fully connected network. In the fuzzy global pooling layer, the generated feature maps are transferred into the fuzzy domain by two different fuzzification methods. One of the fuzzy feature maps exploits the uncertainty information of histopathologic images, and the other keeps the original information. Furthermore, the fuzzy feature maps are processed by using Min-max operations. The experiments verified that the proposed method could always find the maximum fuzzy entropy and exploit and present the uncertainty of histopathologic images well. The experiments using the benchmark dataset demonstrate that the proposed model becomes more accurate and outperforms the existing models including the benchmark models. Compared to the benchmark model with 89.8% of accuracy, 96.3% of AUC, and 0.260 of negative log-likelihood loss, the proposed model obtained 91.7% of accuracy, 97.2% of AUC, and 0.214 of negative log-likelihood loss.

Tumor saliency estimation for breast ultrasound images via breast anatomy modeling.

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound (BUS) image due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model the generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues. The extensive experiments demonstrate that the proposed approach obtains more accurate foreground and background maps with breast anatomy; especially, for the images having large or small tumors. Meanwhile, the new objective function can handle the images without tumors. The newly proposed method achieves state-of-the-art performance comparing to eight tumor saliency estimation approaches using two BUS datasets.

Negative Impact of Sadness on Response Inhibition in Females: An Explicit Emotional Stop Signal Task fMRI Study.

Response inhibition is a critical cognitive ability underlying executive control over reactions to external cues, or inner requirements. Previous studies suggest that high arousal negative emotions (e.g., anger or fear) could impair response inhibition in implicit emotional stop signal tasks (eSSTs). However, studies exploring how low arousal negative emotions (e.g., sadness) influence response inhibition remain sparse. In the current study, 20 female college students performed an explicit eSST to explore the influence of sadness on response inhibition and its neural mechanism. Participants are instructed to press a button to sad or neutral facial stimuli while inhibiting their response during the presentation of a stop signal. Results showed that compared with neutral stimuli, sad stimuli were related to increased stop signal reaction time (SSRT) (i.e., worse response inhibition). Compared with neutral condition, higher activation during sad condition was found within the right superior frontal gyrus (SFG), right insula, right middle cingulate cortex (MCC), bilateral superior temporal gyrus (STG), left lingual gyrus, and right motor cortex. These findings indicated that sadness, like other negative emotions, may impair response inhibition in an explicit way and highlight the explicit eSST as a new paradigm to investigate the subtle interaction between negative emotion processing and cognitive control.

An improved quantitative measurement for thyroid cancer detection based on elastography.

OBJECTIVE: To evaluate color thyroid elastograms quantitatively and objectively. MATERIALS AND METHODS: 125 cases (56 malignant and 69 benign) were collected with the HITACHI Vision 900 system (Hitachi Medical System, Tokyo, Japan) and a liner-array-transducer of 6-13MHz. Standard of reference was cytology (FNA-fine needle aspiration) or histology (core biopsy). The original color thyroid elastograms were transferred from red, green, blue (RGB) color space to hue, saturation, value (HSV) color space. Then, hard area ratio was defined. Finally, a SVM classifier was used to classify thyroid nodules into benign and malignant. The relation between the performance and hard threshold was fully investigated and studied. RESULTS: The classification accuracy changed with the hard threshold, and reached maximum (95.2%) at some values (from 144 to 152). It was higher than strain ratio (87.2%) and color score (83.2%). It was also higher than the one of our previous study (93.6%). CONCLUSION: The hard area ratio is an important feature of elastogram, and appropriately selected hard threshold can improve classification accuracy.