A self-supervised deep Riemannian representation to classify parkinsonian fixational patterns.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, and it remains incurable. Currently there is no definitive biomarker for detecting PD, measuring its severity, or monitoring of treatments. Recently, oculomotor fixation abnormalities have emerged as a sensitive biomarker to discriminate Parkinsonian patterns from a control population, even at early stages.

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).

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.

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

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.

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.

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.

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%.

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).

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.

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.

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.

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.

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.

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).

Natural family-free genomic distance.

Background: A classical problem in comparative genomics is to compute the rearrangement distance, that is the minimum number of large-scale rearrangements required to transform a given genome into another given genome. The traditional approaches in this area are family-based, i.e.

Towards understanding spatio-temporal parkinsonian patterns from salient regions of a 3D convolutional network.

Gait motion patterns such as step length, flexed posture, absent arm swing and bradykinesia, constitute the main source of information to describe and quantify Parkinson disease. Nevertheless, such quantification is commonly developed under marker based protocols, losing natural motion gestures, and only taking into account a limited description of the locomotion process. This work introduces a 3D convolutional gait representation, that uses markerless video sequences to automatically predict parkinsonian behaviours.

An online deep convolutional polyp lesion prediction over Narrow Band Imaging (NBI).

Polyps, represented as abnormal protuberances along intestinal track, are the main biomarker to diagnose gastrointestinal cancer. During routine colonoscopies such polyps are localized and coarsely characterized according to microvascular and surface textural patterns. Narrow-band imaging (NBI) sequences have emerged as complementary technique to enhance description of suspicious mucosa surfaces according to blood vessels architectures.

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants.

Background: Computationally inferred ancestral genomes play an important role in many areas of genome research. We present an improved workflow for the reconstruction from highly diverged genomes such as those of plants.

Results: Our work relies on an established workflow in the reconstruction of ancestral plants, but improves several steps of this process.

Computing the family-free DCJ similarity.

Background: The genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity.

Approximating the DCJ distance of balanced genomes in linear time.

Background: Rearrangements are large-scale mutations in genomes, responsible for complex changes and structural variations. Most rearrangements that modify the organization of a genome can be represented by the double cut and join (DCJ) operation. Given two balanced genomes, i.