Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks.

The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.

Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images.

BACKGROUND: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using artificial intelligence. However, only two papers have dealt with complex BI-RADS classifications. PURPOSE: This study addresses the automatic classification of breast lesions into binary classes (benign vs. malignant) and multiple BI-RADS classes based on a single ultrasonographic image. Achieving this task should reduce the subjectivity of an individual operator's assessment. MATERIALS AND METHODS: Automatic image segmentation methods (PraNet, CaraNet and FCBFormer) adapted to the specific segmentation task were investigated using the U-Net model as a reference. A new classification method was developed using an ensemble of selected segmentation approaches. All experiments were performed on publicly available BUS B, OASBUD, BUSI and private datasets. RESULTS: FCBFormer achieved the best outcomes for the segmentation task with intersection over union metric values of 0.81, 0.80 and 0.73 and Dice values of 0.89, 0.87 and 0.82, respectively, for the BUS B, BUSI and OASBUD datasets. Through a series of experiments, we determined that adding an extra 30-pixel margin to the segmentation mask counteracts the potential errors introduced by the segmentation algorithm. An assembly of the full image classifier, bounding box classifier and masked image classifier was the most accurate for binary classification and had the best accuracy (ACC; 0.908), F1 (0.846) and area under the receiver operating characteristics curve (AUROC; 0.871) in the BUS B and ACC (0.982), F1 (0.984) and AUROC (0.998) in the UCC BUS datasets, outperforming each classifier used separately. It was also the most effective for BI-RADS classification, with ACC of 0.953, F1 of 0.920 and AUROC of 0.986 in UCC BUS. Hard voting was the most effective method for dichotomous classification. For the multi-class BI-RADS classification, the soft voting approach was employed. CONCLUSIONS: The proposed new classification approach with an ensemble of segmentation and classification approaches proved more accurate than most published results for binary and multi-class BI-RADS classifications.

Antipsychotic drug prescription sequence analysis in relation to death occurrence and cardiometabolic drug usage: A retrospective longitudinal study

Background and objectivesThe potential role of antipsychotics in increasing cardiovascular risk of mortality is still debated. The aim of this study was to assess the death risk associated with sequences of first-generation antipsychotic (FGA) and second-generation antipsychotic (SGA) prescriptions, including clozapine and lithium, and drugs for cardiometabolic diseases.MethodsWe conducted a retrospective longitudinal analysis involving 84,881 patients who received antipsychotics between 2008 and 2012. Data on deaths were collected from the National Death Registry. The sequence creation was performed according to an algorithm that iterates prescriptions in chronological order and appends them to the end of the patient's prescription sequence. Fuzzy set qualitative comparative analysis (FsQCA) was also used to produce causal combinations of conditions that best lead to survival.ResultsThere were 1,095,518 antipsychotic prescriptions and 16,010 deaths among antipsychotic users. Among the reimbursement data, 85,272 drug sequences were identified. The most prevalent sequence consisted of FGA (69.1%). Subsequent groups consisted of FGA, followed by SGA (13.1%) and SGA-only (12.3%) sequences. The highest occurrence of death and cardiometabolic drug use after introducing antipsychotic treatment was observed for clozapine. The FsQCA analysis revealed the highest coverage for combinations of young age with FGA (40.6%) or with no cardiometabolic risk factors drug therapy (39.5%).ConclusionThe sequence analysis suggests that clozapine is associated with an increased death risk compared to FGA and SGA. (AU)