Incremental Few-Shot Instance Segmentation

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly

Underwater Live Fish Recognition Using a Balance-Guaranteed Optimized Tree

Abstract. Live fish recognition in the open sea is a challenging multiclass classification task. We propose a novel method to recognize fish in an unrestricted natural environment recorded by underwater cameras. This method extracts 66 types of features, which are a combination of color, shape and texture properties from different parts of the fish and reduce the feature dimensions with forward sequential feature selection (FSFS) procedure. The selected features of the FSFS are used by an SVM. We present a Balance-Guaranteed Optimized Tree (BGOT) to control the error accumulation in hierarchical classification and, therefore, achieve better performance. A BGOT of 10 fish species is automatically constructed using the inter-class similarities and a heuristic method. The proposed BGOT-based hierarchical classification method achieves about 4 % better accuracy compared to state-of-the-art techniques on a live fish image dataset.

Driving pressure during general anesthesia for open abdominal surgery (DESIGNATION) : study protocol of a randomized clinical trial

Background Intraoperative driving pressure (Delta P) is associated with development of postoperative pulmonary complications (PPC). When tidal volume (V-T) is kept constant, Delta P may change according to positive end-expiratory pressure (PEEP)-induced changes in lung aeration. Delta P may decrease if PEEP leads to a recruitment of collapsed lung tissue but will increase if PEEP mainly causes pulmonary overdistension. This study tests the hypothesis that individualized high PEEP, when compared to fixed low PEEP, protects against PPC in patients undergoing open abdominal surgery. Methods The "Driving prESsure durIng GeNeral AnesThesIa for Open abdomiNal surgery trial" (DESIGNATION) is an international, multicenter, two-group, double-blind randomized clinical superiority trial. A total of 1468 patients will be randomly assigned to one of the two intraoperative ventilation strategies. Investigators screen patients aged >= 18 years and with a body mass index <= 40 kg/m(2), scheduled for open abdominal surgery and at risk for PPC. Patients either receive an intraoperative ventilation strategy with individualized high PEEP with recruitment maneuvers (RM) ("individualized high PEEP") or one in which PEEP of 5 cm H2O without RM is used ("low PEEP"). In the "individualized high PEEP" group, PEEP is set at the level at which Delta P is lowest. In both groups of the trial, V-T is kept at 8 mL/kg predicted body weight. The primary endpoint is the occurrence of PPC, recorded as a collapsed composite of adverse pulmonary events. Discussion DESIGNATION will be the first randomized clinical trial that is adequately powered to compare the effects of individualized high PEEP with RM versus fixed low PEEP without RM on the occurrence of PPC after open abdominal surgery. The results of DESIGNATION will support anesthesiologists in their decisions regarding PEEP settings during open abdominal surgery

Uncertainty-aware estimation of population abundance using machine learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the po-tential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification un-certainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine Learning technologies, and our visualizations further support their transfer to end-users

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (&gt;= 65 years; estimated glomerular filtration rate &lt;= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off &lt;= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men

SHREC 2020: 3D Point Cloud Semantic Segmentation for Street Scenes

Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, its cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with over 2 million points represents a street scene and includes a couple of objects. There are five meaningful classes: building, car, ground, pole and vegetation. We aim at localizing and segmenting semantic objects from these large-scale 3D point clouds. Four groups contributed their results with different methods. The results show that learning-based methods are the trend and one of them achieves the best performance on both Overall Accuracy and mean Intersection over Union. Next to the learning-based methods, the combination of hand-crafted detectors are also reliable and rank second among comparison algorithms