Dual-process model of courage.

Courage is one of the most significant psychological constructs for society, but not one of the most frequently studied. This paper presents a process model of courage consisting of decision-based pathways by which one comes to enact a courageous action. We argue the process of courage begins with a trigger involving an actor(s) and a situation(s). The actor(s) then engage(s) in four key assessments concerning (a) immediacy of the situation, (b) meaningfulness, value, and relevance to the actor, (c) adequacy of efficacy to act, and (d) decision to act with courage. The central component of this process entails an approach-avoidance conflict involving assessments of perceived risks and potential noble outcomes of acting with courage. The decision to act may result in courageous actions assuming it satisfies the four elements: intentionality, objective and substantial risk, a noble purpose, and meaning in time and place. Courageous actions have consequences. Finally, the consequences shape the actors' experience, which feeds into the trigger, closing the loop. Potential moderators of the courage process as well as potential tests of the model have been discussed.

A non-aligned translation with a neoplastic classifier regularization to include vascular NBI patterns in standard colonoscopies.

Polyp vascular patterns are key to categorizing colorectal cancer malignancy. These patterns are typically observed in situ from specialized narrow-band images (NBI). Nonetheless, such vascular characterization is lost from standard colonoscopies (the primary attention mechanism). Besides, even for NBI observations, the categorization remains biased for expert observations, reporting errors in classification from 59.5% to 84.2%. This work introduces an end-to-end computational strategy to enhance in situ standard colonoscopy observations, including vascular patterns typically observed from NBI mechanisms. These retrieved synthetic images are achieved by adjusting a deep representation under a non-aligned translation task from optical colonoscopy (OC) to NBI. The introduced scheme includes an architecture to discriminate enhanced neoplastic patterns achieving a remarkable separation into the embedding representation. The proposed approach was validated in a public dataset with a total of 76 sequences, including standard optical sequences and the respective NBI observations. The enhanced optical sequences were automatically classified among adenomas and hyperplastic samples achieving an F1-score of 0.86%. To measure the sensibility capability of the proposed approach, serrated samples were projected to the trained architecture. In this experiment, statistical differences from three classes with a ρ-value <0.05 were reported, following a Mann-Whitney U test. This work showed remarkable polyp discrimination results in enhancing OC sequences regarding typical NBI patterns. This method also learns polyp class distributions under the unpaired criteria (close to real practice), with the capability to separate serrated samples from adenomas and hyperplastic ones.

Talent identification research: a bibliometric study from multidisciplinary and global perspectives.

This paper describes the general status, trends, and evolution of research on talent identification across multiple fields globally over the last 80 years. Using Scopus and Web of Science databases, we explored patterns of productivity, collaboration, and knowledge structures in talent identification (TI) research. Bibliometric analysis of 2,502 documents revealed talent identification research is concentrated in the fields of management, business, and leadership (~37%), sports and sports science (~20%), and education, psychology, and STEM (~23%). Whereas research in management and sports science has occurred independently, research in psychology and education has created a bridge for the pollination of ideas across fields. Thematic evolution analysis indicates that TI has well developed motor and basic research themes focused on assessment, cognitive abilities, fitness, and youth characteristics. Motor themes in management and sports science bring attention to talent management beyond TI. Emerging research focuses on equity and diversity as well as innovation in identification and technology-based selection methods. Our paper contributes to the development of the body of TI research by (a) highlighting the role of TI across multiple disciplines, (b) determining the most impactful sources and authors in TI research, and (c) tracing the evolution of TI research which identifies gaps and future opportunities for exploring and developing TI research and its broader implications for other areas of research and society.

A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies.

The key component of stroke diagnosis is the localization and delineation of brain lesions, especially from MRI studies. Nonetheless, this manual delineation is time-consuming and biased by expert opinion. The main purpose of this study is to introduce an autoencoder architecture that effectively integrates cross-attention mechanisms, together with hierarchical deep supervision to delineate lesions under scenarios of remarked unbalance tissue classes, challenging geometry of the shape, and a variable textural representation. This work introduces a cross-attention deep autoencoder that focuses on the lesion shape through a set of convolutional saliency maps, forcing skip connections to preserve the morphology of affected tissue. Moreover, a deep supervision training scheme was herein adapted to induce the learning of hierarchical lesion details. Besides, a special weighted loss function remarks lesion tissue, alleviating the negative impact of class imbalance. The proposed approach was validated on the public ISLES2017 dataset outperforming state-of-the-art results, achieving a dice score of 0.36 and a precision of 0.42. Deeply supervised cross-attention autoencoders, trained to pay more attention to lesion tissue, are better at estimating ischemic lesions in MRI studies. The best architectural configuration was achieved by integrating ADC, TTP and Tmax sequences. The contribution of deeply supervised cross-attention autoencoders allows better support the discrimination between healthy and lesion regions, which in consequence results in favorable prognosis and follow-up of patients.

The Evolution of Intelligence: Analysis of the Journal of Intelligence and Intelligence.

What are the current trends in intelligence research? This parallel bibliometric analysis covers the two premier journals in the field: Intelligence and the Journal of Intelligence (JOI) between 2013 and 2022. Using Scopus data, this paper extends prior bibliometric articles reporting the evolution of the journal Intelligence from 1977 up to 2018. It includes JOI from its inception, along with Intelligence to the present. Although the journal Intelligence's growth has declined over time, it remains a stronghold for traditional influential research (average publications per year = 71.2, average citations per article = 17.07, average citations per year = 2.68). JOI shows a steady growth pattern in the number of publications and citations (average publications per year = 33.2, average citations per article = 6.48, total average citations per year = 1.48) since its inception in 2013. Common areas of study across both journals include cognitive ability, fluid intelligence, psychometrics-statistics, g-factor, and working memory. Intelligence includes core themes like the Flynn effect, individual differences, and geographic IQ variability. JOI addresses themes such as creativity, personality, and emotional intelligence. We discuss research trends, co-citation networks, thematic maps, and their implications for the future of the two journals and the evolution and future of the scientific study of intelligence.

Weakly Supervised Polyp Segmentation from an Attention Receptive Field Mechanism.

Colorectal cancer is the third most incidence cancer world-around. Colonoscopies are the most effective resource to detect and segment abnormal polyp masses, considered as the main biomarker of this cancer. Nonetheless, some recent clinical studies have revealed a polyp miss rate up to 26% during the clinical routine. Also, the expert bias introduced during polyp shape characterization may induce to false-negative diagnosis. Current computational approaches have supported polyp segmentation but over controlled scenarios, where polyp frames have been labeled by an expert. These supervised representations are fully dependent of well-segmented polyps, in crop sequences that always report these masses. This work introduces an attention receptive field mechanism, that robustly recover the polyp shape, by learning non-local pixel relationship. Besides this deep representation is learning from a weakly supervised scheme that includes unlabeled background frames, to discriminate polyps from near structures like intestinal folds. The achieved results outperform state-of-the-art approaches achieving a 95.1% precision in the public CVC-Colon DB, with also competitive performance on other datasets. Clinical relevance-The work address a novel strategy to support segmentation tools in a clinical routine with redundant background over colonoscopy sequences.

A Spatio-Temporal Hypomimic Deep Descriptor to Discriminate Parkinsonian Patients.

The hypomimia is a main clinical sign of Parkinson disease that describes motor patterns associated with the reduction and progressive loss of facial expression. This clinical sign constitutes a main biomarker to support diagnosis, even at early stages, and to establish progression and description of the disease. In clinical routine, the evaluation of such signs remains subjective or limited to the description of some landmarks that poorly describe little expressions correlated with the disease. This work introduces a new digital biomarker, expressed as a spatio-temporal convolutional representation that learns facial movement patterns to discriminate between Parkinson and control patients. The proposed architecture builds a representation through 3D convolutional layers, which are integrated from inception modules, achieving salient maps of face expression activations. This approach was validated in a retrospective study that includes 16 Parkinson patients and 16 control subjects. The architecture achieves an average accuracy of 91.87% using 480 video sequences in classification condition task. Clinical relevance- A digital descriptor that quantify ges-tural face signatures described from a deep spatio-temporal representation with the capability to discriminate Parkinsonian patients.

A Non-Aligned Deep Representation to Enhance Standard Colonoscopy Observations from Vascular Narrow Band Polyp Patterns.

Colorectal cancer (CRC) was responsible during 2020 for about one million deaths worldwide. Polyps are protuberance masses, observed in routine colonoscopies, that constitute the main CRC biomarker. Nonetheless, one of the best alternatives to the polyp malignancy classification is the vascular pattern analysis, typically observed from specialized narrow-band images (NBI). Even worst, these patterns are only characterized from gastroenterologist observations, introducing subjectivity and being prone to diagnostic errors, with misclassi-fications ranging from 59.5 % to 84.2 %. This work introduces a non-aligned and bi-directional deep projection between optical colonoscopy (OC) and NBI sequences, to recover enhanced OC sequences, integrating vascular patterns, that allow better dis-crimination among adenomas, hyperplastic and serrated polyps. This self-supervised representation help with misclassification in standard OC observations. The validation was performed on a total of 76 OC and 76 NBI sequences, achieving a gain of 22.34% w.r.t descriptors computed from raw OC. Clinical relevance- A deep representation that enhances standard OC observations associating vascularity to the polyps to discriminate among adenomas hyperplastic and serrated polyps.

A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns.

Parkinson is the second most common neurodegenerative disease, mainly related to progressive locomotor alterations caused by dopamine deficiency. The gait kinematic is a principal disease biomarker that associates patterns like the step length, flexed posture, and bradykinesia with disease progression. Nonetheless, these patterns are analyzed from invasive setups that only capture coarse dynamics at advanced disease stages. This work introduces a very compact and robust deep representation that effectively learns a Riemannian manifold to describe locomotor parkinsonian patterns. Contrary to traditional deep strategies, the presented framework fully explores data geometry from input mean covariance matrices representing video sequences. These symmetric positive definite (SPD) matrices lie in a Riemannian manifold. Then such matrices are projected to the SPD net, which learns a bank of SPD matrices from a non-linear training, that may be exploited in a hierarchical composition from a set of layers. In a final layer, a projection in a Euclidean space allows learning the discriminatory patterns of PD w.r.t control population. In a retrospective study with a total of 22 patients (11 Parkinson's and 11 controls), the proposed approach achieves a remarkable classification between Parkinson's and control video sequences, correctly labeling all Parkinson's patients, and outperforming typical 3D convolutional representations. Clinical relevance- The study of gait parkinsonian descrip-tors computed from a Riemannian geometry, built from a deep representation.

A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation.

Parkinson's Disease (PD), the second most common neurodegenerative disorder, is associated with voluntary movement disorders caused by progressive dopamine deficiency. Gait motor alterations constitute a main tool to diagnose, characterize and personalize treatments. Nonetheless, such evaluation is biased by expert observations, reporting a false positive diagnosis up to 24%. Learning computational tools are recently emerged as potential alternatives to support diagnosis and to quantify kinematic patterns during locomotion. Nonetheless, such learning schemes required a large amount of balanced and stratified data examples, which may result unrealistic in clinical scenarios. This work introduces a self-supervised generative representation to discover gait-motion related patterns, under the pretext of video reconstruction and an anomaly detection framework. From the learned scheme, it is recovered a hidden embedding gait descriptor that constitutes a digital biomarker, allowing to discover PD differences regarding a control population. The proposed approach was validated with 11 PD patients (H&Y scale between 2.5 and 3.0) and 11 control subjects, and trained with only control population, achieving an AUC of 99.4% in the classification task. Clinical Relevance- A digital biomarker that helps in the diagnosis of PD using videos of a patient's gait to capture important and relevant motion patterns to avoid subjectivity when an expert made a diagnosis.

A deep CT to MRI unpaired translation that preserve ischemic stroke lesions.

Stroke is the second-leading cause of death world around. The immediate attention is key to patient prognosis. Ischemic stroke diagnosis typically involves neuroimaging studies (MRI and CT scans) and clinical protocols to characterize lesions and support decisions about treatment to be administered to the patient. Nowadays, multiparametric MRI images are the standard tool to visualize core and penumbra of ischemic stroke, supporting diagnosis and lesion prognosis. Specially, DWI modality (Diffusion Weighted Imaging) allows to quantify the cellular density of the tissue, and therefore allowing to quantify the lesion aggressiveness, and the recognition of micro-circulation properties. Nevertheless, MRI availability at hospitals is not widespread, and acquisition require special conditions requiring considerable time. Contrary, CT scans commonly have major availability but brain structures are poorly delineated, and even worse, ischemic lesions are only visible at advanced stages of the disease. This work introduces a deep generative strategy that allows ischemic stroke lesion translation over synthetic DWI-MRI images. This encoder-decoder architecture, include U-net modules, hierarchically organized, with inter-level connections that preserve brain structures, while codifying an embedding representation. Then a cyclic loss was here implemented to receive CT inputs and decode DWI-MRI images. To avoid mode collapse, this learning is inversely propagated, i.e., from synthetic DWI-MRI images to original CT-scans. Finally, an embedding projection is recovered to show a proper lesion-slice discrimination, regarding control studies. Clinical relevance- To recover synthetic DWI-MRI that preserved ischemic lesion using CT scans as an input and following an unpaired image translation setup.

An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer.

Objective.Multi-parametric magnetic resonance imaging (MP-MRI) has played an important role in prostate cancer diagnosis. Nevertheless, in the clinical routine, these sequences are principally analyzed from expert observations, which introduces an intrinsic variability in the diagnosis. Even worse, the isolated study of these MRI sequences trends to false positive detection due to other diseases that share similar radiological findings. Hence, the main objective of this study was to design, propose and validate a deep multimodal learning framework to support MRI-based prostate cancer diagnosis using cross-correlation modules that fuse MRI regions, coded from independent MRI parameter branches.Approach.This work introduces a multimodal scheme that integrates MP-MRI sequences and allows to characterize prostate lesions related to cancer disease. For doing so, potential 3D regions were extracted around expert annotations over different prostate zones. Then, a convolutional representation was obtained from each evaluated sequence, allowing a rich and hierarchical deep representation. Each convolutional branch representation was integrated following a special inception-like module. This module allows a redundant non-linear integration that preserves textural spatial lesion features and could obtain higher levels of representation.Main results.This strategy enhances micro-circulation, morphological, and cellular density features, which thereafter are integrated according to an inception late fusion strategy, leading to a better differentiation of prostate cancer lesions. The proposed strategy achieved a ROC-AUC of 0.82 over the PROSTATEx dataset by fusing regions ofKtransand apparent diffusion coefficient (ADC) maps coded from DWI-MRI.Significance.This study conducted an evaluation about how MP-MRI parameters can be fused, through a deep learning representation, exploiting spatial correlations among multiple lesion observations. The strategy, from a multimodal representation, learns branches representations to exploit radio-logical findings from ADC andKtrans. Besides, the proposed strategy is very compact (151 630 trainable parameters). Hence, the methodology is very fast in training (3 s for an epoch of 320 samples), being potentially applicable in clinical scenarios.

Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer.

Clinically significant regions (CSR), captured over multi-parametric MRI (mp-MRI) images, have emerged as a potential screening test for early prostate cancer detection and characterization. These sequences are able to quantify morphology, micro-circulation, and cellular density patterns that might be related to cancer disease. Nonetheless, this evaluation is mainly carried out by expert radiologists, introducing inter-reader variability in the diagnosis. Therefore, different deep learning models were proposed to support the diagnosis, but a proper representation of prostate lesions remains limited due to the non-alignment among sequences and the dependency of considerable amounts of labeled data for learning. The main limitation of such representation lies in the cross-entropy minimization that only exploits inter-class variation, being insufficient data augmentation and transfer learning strategies. This work introduces a Supervised Contrastive Learning (SCL) strategy that fully exploits the inter and intra-class variability of prostate lesions to robustly represent MRI regions. This strategy extracts lesion sample tuples, with positive and negative labels, regarding a query lesion. Such tuples are involved into an easy-positive, and semi-hard negative mining to project samples that better update the deep representation. The proposed learning strategy achieved an average ROC-AVC of 0.82, to characterize prostate cancer in MRI, using only the 60% of the available annotated data. Clinical relevance - A robust learning scheme that properly finds representations in limited data scenarios to classify clinically significant MRI regions on prostate cancer.

Criterion-Referenced Assessment of Intelligence as Adaptation to the Environment: Is It Possible, Plausible, or Practical?

Criterion-referenced testing is usually applied to the assessment of achievement. In this article, we suggest how it can also be applied to the assessment of adaptive intelligence, that is, intelligence as adaptation to the environment. In the era of the Anthropocene, we argue that adaptive intelligence is what is most important not only for individual success, but also for success in terms of preservation of the world as we know it. We define criterion-referenced testing and compare it to norm-referenced testing. We then discuss two kinds of scoring of criterion-referenced testing, namely, with respect to external criteria and with respect to internal (theory-based) criteria. We then discuss past research on intelligence that could be viewed as criterion-referenced. Finally, we suggest how criterion-referencing could be applied to the assessment of adaptive intelligence.

A multitask deep representation for Gleason score classification to support grade annotations.

The Gleason grade system is the main standard to quantify the aggressiveness and progression of prostate cancer. Currently, exists a high disagreement among experts in the diagnosis and stratification of this disease. Deep learning models have emerged as an alternative to classify and support experts automatically. However, these models are limited to learn a rigid stratification rule that can be biased during training to a specific observer. Therefore, this work introduces an embedding representation that integrates an auxiliary task learning to deal with the high inter and intra appearance of the Gleason system. The proposed strategy implements as a main task a triplet loss scheme that builds a feature embedding space with respect to batches of positive and negative histological training patches. As an auxiliary task is added a cross-entropy that helps with inter-class variability of samples while adding robust representations to the main task. The proposed approach shows promising results achieving an average accuracy of 66% and 64%, for two experts without statistical difference. Additionally, reach and average accuracy of 73% in patches where both pathologists are agree, showing the robustness patterns learning from the approach.

A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation.

Cardiac cine-MRI is one of the most important diagnostic tools used to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is poorly exploited and remains highly dependent on the observer's expertise. This work introduces an imaging cardiac disease representation, coded as an embedding vector, that fully exploits hidden mapping between the latent space and a generated cine-MRI data distribution. The resultant representation is progressively learned and conditioned by a set of cardiac conditions. A generative cardiac descriptor is achieved from a progressive generative-adversarial network trained to produce MRI synthetic images, conditioned to several heart conditions. The generator model is then used to recover a digital biomarker, coded as an embedding vector, following a backpropagation scheme. Then, an UMAP strategy is applied to build a topological low dimensional embedding space that discriminates among cardiac pathologies. Evaluation of the approach is carried out by using an embedded representation as a potential disease descriptor in 2296 pathological cine-MRI slices. The proposed strategy yields an average accuracy of 0.8 to discriminate among heart conditions. Furthermore, the low dimensional space shows a remarkable grouping of cardiac classes that may suggest its potential use as a tool to support diagnosis. The learned progressive and generative representation, from cine-MRI slices, allows retrieves and coded complex descriptors that results useful to discriminate among heart conditions. The cardiac disease representation expressed as a hidden embedding vector could potentially be used to support cardiac analysis on cine-MRI sequences.

A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a motor neurodegenerative disease principally manifested by motor disabilities, such as postural instability, bradykinesia, tremor, and stiffness. In clinical practice, there exist several diagnostic rating scales that coarsely allow the measurement, characterization and classification of disease progression. These scales, however, are only based on strong changes in kinematic patterns, and the classification remains subjective, depending on the expertise of physicians. In addition, even for experts, disease analysis based on independent classical motor patterns lacks sufficient sensitivity to establish disease progression. Consequently, the disease diagnosis, stage, and progression could be affected by misinterpretations that lead to incorrect or inefficient treatment plans. This work introduces a multimodal non-invasive strategy based on video descriptors that integrate patterns from gait and eye fixation modalities to assist PD quantification and to support the diagnosis and follow-up of the patient. The multimodal representation is achieved from a compact covariance descriptor that characterizes postural and time changes of both information sources to improve disease classification. METHODS: A multimodal approach is introduced as a computational method to capture movement abnormalities associated with PD. Two modalities (gait and eye fixation) are recorded in markerless video sequences. Then, each modality sequence is represented, at each frame, by primitive features composed of (1) kinematic measures extracted from a dense optical flow, and (2) deep features extracted from a convolutional network. The spatial distributions of these characteristics are compactly coded in covariance matrices, making it possible to map each particular dynamic in a Riemannian manifold. The temporal mean covariance is then computed and submitted to a supervised Random Forest algorithm to obtain a disease prediction for a particular patient. The fusion of the covariance descriptors and eye movements integrating deep and kinematic features is evaluated to assess their contribution to disease quantification and prediction. In particular, in this study, the gait quantification is associated with typical patterns observed by the specialist, while ocular fixation, associated with early disease characterization, complements the analysis. RESULTS: In a study conducted with 13 control subjects and 13 PD patients, the fusion of gait and ocular fixation, integrating deep and kinematic features, achieved an average accuracy of 100% for early and late fusion. The classification probabilities show high confidence in the prediction diagnosis, the control subjects probabilities being lower than 0.27 with early fusion and 0.3 with late fusion, and those of the PD patients, being higher than 0.62 with early fusion and 0.51 with late fusion. Furthermore, it is observed that higher probability outputs are correlated with more advanced stages of the disease, according to the H&Y scale. CONCLUSIONS: A novel approach for fusing motion modalities captured in markerless video sequences was introduced. This multimodal integration had a remarkable discrimination performance in a study conducted with PD and control patients. The representation of compact covariance descriptors from kinematic and deep features suggests that the proposed strategy is a potential tool to support diagnosis and subsequent monitoring of the disease. During fusion it was observed that devoting major attention to eye fixational patterns may contribute to a better quantification of the disease, especially at stage 2.

Learning a Triplet Embedding Distance to Represent Gleason Patterns.

Gleason grade stratification is the main histological standard to determine the severity and progression of prostate cancer. Nonetheless, there is a high variability on disease diagnosis among expert pathologists (kappa lower than 0.44). End-to-end deep representations have recently deal with the automatic classification of Gleason grades, where each grade is limited to namely code high-visual-variability sharing patterns among classes. Such limitation on models may be attributed to the relatively few labels to train the representation, as well as, to the natural imbalanced sets, available in clinical scenarios. To overcome such limitation, this work introduces a new embedding representation that learns intra and inter-Gleason relationships from more challenging class samples (grades tree and fourth). The proposed strategy implements a triplet loss scheme building a hidden embedding space that correctly differentiates close Gleason levels. The proposed approach shows promising results achieving an average accuracy of 74% to differentiate between degrees three and four. For classification of all degrees, the proposed approach achieves an average accuracy of 62%.

Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors.

Cardiac cine-MRI is one of the most important diagnostic tools for characterizing heart-related pathologies. This imaging technique allows clinicians to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is highly dependent on the observer expertise and a high inter-reader variability is frequently observed. Alternatively, the ejection fraction, a quantitative heart dynamic measure, is used to identify potential cardiac diseases. Unfortunately, this type of measurement is insufficient to distinguish among different cardiac pathologies. This quantification does not exploit all the heart functional information conveyed by cine-MRI sequences. Automatic image analysis might help to identify visual patterns associated with cardiac diseases in the cine-MRI sequences and highlight potential biomarkers. This paper introduces a conditional generative adversarial network that learns a mapping between the latent space and a generated cine-MRI data distribution involving information from five different cardiac pathologies. This net is guided from the left ventricle segmentation and the velocity field that is computed as prior information to focus on the deep representation of salient cardiac patterns. Once the deep neural networks are trained, a set of validation cine-MRI slices is represented in the embedding space. The associated embedding descriptor, in the latent space, is found by minimizing a reconstruction error in the generator output. We evaluated the obtained embedded representation as a disease marker by using different classification models in 16000 pathological cine-MRI slices. The representation retrieved by using the best conditional generative model configuration was used on the classifier models yielding an average accuracy of 90.04% and an average F1-score of 89.97% in the classification task.Clinical relevance-Construction of a topological embedding space, from generative representation, that fully exploits hidden relationships of cine-MRI and represent cardiac diseases.

Visualising and quantifying relevant parkinsonian gait patterns using 3D convolutional network.

Parkinson's disease (PD) lacks a definitive diagnosis, with the observation of motion patterns being the main method of characterizing disease progression and planning patient treatments. Among PD observations, gait motion patterns, such as step length, flexed posture, and bradykinesia, support the characterization of disease progression. However, this analysis is usually performed with marker-based protocols, which affect the gait and localized segment patterns during locomotion. This work introduces a 3D convolutional gait representation for automatic PD classification that identifies the spatio-temporal patterns used for classification. This approach allows us to obtain an explainable model that classifies markerless sequences and describes the main learned spatio-temporal regions associated with abnormal patterns in a particular video. Initially, a spatio-temporal convolutional network is trained from a set of raw videos and optical flow fields. Then, a PD prediction is obtained from the motion patterns learned by the trained model. Finally, saliency maps, which highlight abnormal motion patterns, are obtained by retro-propagating the output prediction up to the input volume through two different stages: an embedded back-tracking and a pseudo-deconvolution process. From a total of 176 videos from 22 patients, the resulting salient maps highlight lower limb patterns possibly related to step length and speed. In control subjects, the saliency maps highlight the head and trunk posture. The proposed approach achieved an average accuracy score of 94.89%.