On the Use of the Platelet Activity State Assay for the In Vitro Quantification of Platelet Activation in Blood Recirculating Devices for Extracorporeal Circulation.

We designed an experimental setup to characterize the thrombogenic potential associated with blood recirculating devices (BRDs) used in extracorporeal circulation (ECC). Our methodology relies on in vitro flow loop platelet recirculation experiments combined with the modified-prothrombinase platelet activity state (PAS) assay to quantify the bulk thrombin production rate of circulated platelets, which correlates to the platelet activation (PA) level. The method was applied to a commercial neonatal hollow fiber membrane oxygenator. In analogous hemodynamic environment, we compared the PA level resulting from multiple passes of platelets within devices provided with phosphorylcholine (PC)-coated and noncoated (NC) fibers to account for flow-related mechanical factors (i.e., fluid-induced shear stress) together with surface contact activation phenomena. We report for the first time that PAS assay is not significantly sensitive to the effect of material coating under clinically pertinent flow conditions (500 mL/min), while providing straightforward information on shear-mediated PA dynamics in ECC devices. Being that the latter is intimately dependent on local flow dynamics, according to our results, the rate of thrombin production as measured by the PAS assay is a valuable biochemical marker of the selective contribution of PA in BRDs induced by device design features. Thus, we recommend the use of PAS assay as a means of evaluating the effect of modification of specific device geometrical features and/or different design solutions for developing ECC devices providing flow conditions with reduced thrombogenic impact.

Editorial for the Special Issue on Industrial Machine Learning Applications.

In the rapidly evolving field of industrial machine learning, this Special Issue on Industrial Machine Learning Applications aims to shed light on the innovative strides made toward more intelligent, more efficient, and adaptive industrial processes [...].

Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation.

A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e., the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (UCD). In a nutshell, UCDis operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. In order to mitigate catastrophic forgetting, we contrast features of the new model with features extracted by a frozen model learned at the previous incremental step. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks for incremental semantic segmentation.

Variational Structured Attention Networks for Deep Visual Representation Learning.

Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction and so on, benefiting from their powerful capabilities in visual representation learning. Typically, state of the art models integrate attention mechanisms for improved deep feature representations. Recently, some works have demonstrated the significance of learning and combining both spatial- and channel-wise attentions for deep feature refinement. In this paper, we aim at effectively boosting previous approaches and propose a unified deep framework to jointly learn both spatial attention maps and channel attention vectors in a principled manner so as to structure the resulting attention tensors and model interactions between these two types of attentions. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to VarIational STructured Attention networks (VISTA-Net). We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters. As demonstrated by our extensive empirical evaluation on six large-scale datasets for dense visual prediction, VISTA-Net outperforms the state-of-the-art in multiple continuous and discrete prediction tasks, thus confirming the benefit of the proposed approach in joint structured spatial-channel attention estimation for deep representation learning. The code is available at https://github.com/ygjwd12345/VISTA-Net.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.