Pseudo-class part prototype networks for interpretable breast cancer classification.

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers' attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively 8 % and 18 % . Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images.

Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms.

Background: Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission. Methods: We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC). Results: In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm. Conclusion: XGBoost algorithms can be a promising model for predicting fatal complications following MI.

Anxiety-like behavior induced by allergen is associated with decreased irregularity of breathing pattern in rats.

Allergic rhinitis (AR) is a chronic inflammatory disorder associated with a high prevalence of anxiety symptoms and respiratory disorders that adversely affect the quality of life. Studies have shown that allergen exposure induces anxiety-like behaviors. On the other hand, stress impairs the breathing pattern. However, the effect of stress on respiration and the relationship between anxiety-like behavior and stress-induced changes in breathing pattern has not been evaluated in AR. We assessed the impact of ovalbumin (OVA)-induced anxiety-like behaviors on stress-induced breathing pattern changes. Our findings showed that the allergic rhinitis induced by OVA challenge in sensitized rats induces anxiety-like behavior. Also, we found that stress decreases respiratory irregularity and increases respiratory variability, as well as the synchronization between IBI and RV time-series in AR animals. Moreover, in AR animals, we found a significant positive correlation between anxiety-like behavior and respiratory irregularity under non-stress conditions. Besides, a significant negative correlation was observed under stress conditions. The findings showed that anxiety-related behaviors may contribute to respiratory impairments under stress conditions in AR.

Liver Segmentation in MRI Images using an Adaptive Water Flow Model.

BACKGROUND: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher's attention, it still has some challenging problems in computer-aided diagnosis. OBJECTIVE: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm. MATERIAL AND METHODS: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. RESULTS: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. CONCLUSION: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.

Enhancing Obstructive Apnea Disease Detection Using Dual-Tree Complex Wavelet Transform-Based Features and the Hybrid "K-Means, Recursive Least-Squares" Learning for the Radial Basis Function Network.

BACKGROUND: The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. In this paper, we tested some powerful and low computational signal processing techniques for this task and compared their results with the recent achievements in OSA detection. METHODS: The Dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. From these coefficients, eight non-linear features are extracted and then reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied to the hybrid "K-means, RLS" RBF network which is a low computational rival for the Support vector machine (SVM) networks family. RESULTS AND CONCLUSION: The results showed suitable OSA detection percentage near 96% with a reduced complexity of nearly one third of the previously presented SVM based methods.