Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles.

In IEEE transactions on visualization and computer graphics

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.

Neto Mario Popolin, Paulovich Fernando V

2020-Oct-13

General General

StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics.

In IEEE transactions on visualization and computer graphics

In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.

Chatzimparmpas Angelos, Martins Rafael M, Kucher Kostiantyn, Kerren Andreas

2020-Oct-13

General General

Deep Volumetric Ambient Occlusion.

In IEEE transactions on visualization and computer graphics

We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient occlusion in volumetric data sets, while considering global information provided through the transfer function. The proposed neural network only needs to be executed upon change of this global information, and thus supports real-time volume interaction. Accordingly, we demonstrate DVAO's ability to predict volumetric ambient occlusion, such that it can be applied interactively within direct volume rendering. To achieve the best possible results, we propose and analyze a variety of transfer function representations and injection strategies for deep neural networks. Based on the obtained results we also give recommendations applicable in similar volume learning scenarios. Lastly, we show that DVAO generalizes to a variety of modalities, despite being trained on computed tomography data only.

Engel Dominik, Ropinski Timo

2020-Oct-13

General General

Warp and Learn: Novel Views Generation for Vehicles and Other Objects.

In IEEE transactions on pattern analysis and machine intelligence ; h5-index 127.0

In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image.Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric.In particular, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.This blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input and iv) perform shape transfer to completely different 3D models. Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes.A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view.

Palazzi Andrea, Bergamini Luca, Calderara Simone, Cucchiara Rita

2020-Oct-13

Surgery Surgery

Image-Guided Tethering Spine Surgery with Outcome Prediction using Spatio-Temporal Dynamic Networks.

In IEEE transactions on medical imaging ; h5-index 74.0

Recent fusionless surgical techniques for corrective spine surgery such as Anterior Vertebral Body Growth Modulation (AVBGM) allow to treat mild to severe spinal deformations by tethering vertebral bodies together, helping to preserve lower back flexibility. Forecasting the outcome of AVBGM from skeletally immature patients remains elusive with several factors involved in corrective vertebral tethering, but could help orthopaedic surgeons plan and tailor AVBGM procedures prior to surgery. We introduce an intra-operative framework forecasting the outcomes during AVBGM surgery in scoliosis patients. The method is based on spatial-temporal corrective networks, which learns the similarity in segmental corrections between patients and integrates a long-term shifting mechanism designed to cope with timing differences in onset to surgery dates, between patients in the training set. The model captures dynamic geometric dependencies in scoliosis patients, ensuring long-term dependency with temporal dynamics in curve evolution and integrated features from inter-vertebral disks extracted from T2-w MRI. The loss function of the network introduces a regularization term based on learned group-average piecewise-geodesic path to ensure the generated corrective transformations are coherent with regards to the observed evolution of spine corrections at follow-up exams. The network was trained on 695 3D spine models and tested on 72 operative patients using a set of 3D spine reconstructions as inputs. The spatio-temporal network predicted outputs with errors of 1.8±0.8mm in 3D anatomical landmarks, yielding geometries similar to ground-truth spine reconstructions obtained at one and two year follow-ups and with significant improvements to comparative deep learning and biomechanical models.

Mandel William, Oulbacha Reda, Roy-Beaudry Marjolaine, Parent Stefan, Kadoury Samuel

2020-Oct-13

General General

Safety and feasibility of the PEPPER adaptive bolus advisor and safety system; a randomized control study.

In Diabetes technology & therapeutics ; h5-index 40.0

<b>Background:</b> The Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system provides personalised bolus advice for people with Type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalised carbohydrate recommendations and dynamic bolus insulin constraint. We evaluated the safety and feasibility of the PEPPER system compared to a standard bolus calculator. <b>Methods:</b> This was an open-labelled multicentre randomized controlled cross-over study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12-weeks. Participants then crossed over after a wash-out period. The primary end-point was percentage time in range (TIR, 3.9mmol/L-10.0mmol/L (70-180mg/dL)). Secondary outcomes included glycaemic variability, quality of life, and outcomes on the safety system and insulin recommender. <b>Results:</b> 54 participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 (52.1-67.8) % vs 58.4 (49.6-64.3) % respectively, p=0.27). For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. <b><b>Conclusions:</b></b> The PEPPER system was safe but did not change glycaemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness.

Avari Parizad, Leal Yenny, Herrero Pau, Wos Marzena, Jugnee Narvada, Arnoriaga-Rodríguez María, Thomas Maria, Liu Chengyuan, Massana Quim, Lopez Beatriz, Nita Lucian, Martin Clare, Fernandez-Real J M, Oliver Nick, Fernandez Merce, Reddy Monika

2020-Oct-13