Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis.

The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples.

A Physics-Inspired Deep Learning Framework for an Efficient Fourier Ptychographic Microscopy Reconstruction under Low Overlap Conditions.

Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor.

Fourier ptychographic microscopy image enhancement with bi-modal deep learning.

Digital pathology based on a whole slide imaging system is about to permit a major breakthrough in automated diagnosis for rapid and highly sensitive disease detection. High-resolution FPM (Fourier ptychographic microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis.

Angularly resolved polarization microscopy for birefringent materials with Fourier ptychography.

Polarization light microscopy is a very popular approach for structural imaging in optics. So far these methods mainly probe the sample at a fixed angle of illumination. They are consequently only sensitive to the polarization properties along the microscope optical axis.

Development of NanoLuc-targeting protein degraders and a universal reporter system to benchmark tag-targeted degradation platforms.

Modulation of protein abundance using tag-Targeted Protein Degrader (tTPD) systems targeting FKBP12 (dTAGs) or HaloTag7 (HaloPROTACs) are powerful approaches for preclinical target validation. Interchanging tags and tag-targeting degraders is important to achieve efficient substrate degradation, yet limited degrader/tag pairs are available and side-by-side comparisons have not been performed. To expand the tTPD repertoire we developed catalytic NanoLuc-targeting PROTACs (NanoTACs) to hijack the CRL4 complex and degrade NanoLuc tagged substrates, enabling rapid luminescence-based degradation screening.

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline.

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer's disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG.

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer's disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks.

Diabetic retinopathy detection using red lesion localization and convolutional neural networks.

Detecting the early signs of diabetic retinopathy (DR) is essential, as timely treatment might reduce or even prevent vision loss. Moreover, automatically localizing the regions of the retinal image that might contain lesions can favorably assist specialists in the task of detection. In this study, we designed a lesion localization model using a deep network patch-based approach.

Retinal image quality assessment using deep learning.

Poor-quality retinal images do not allow an accurate medical diagnosis, and it is inconvenient for a patient to return to a medical center to repeat the fundus photography exam. In this paper, a robust automatic system is proposed to assess the quality of retinal images at the moment of the acquisition, aiming at assisting health care professionals during a fundus photography exam. We propose a convolutional neural network (CNN) pretrained on non-medical images for extracting general image features.

Predicting postoperative gait in cerebral palsy.

In this work, postoperative lower limb kinematics are predicted with respect to preoperative kinematics, physical examination and surgery data. Data of 115 children with cerebral palsy that have undergone single-event multilevel surgery were considered. Preoperative data dimension was reduced utilizing principal component analysis.

A dynamic evidential network for fall detection.

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust.

First steps in adaptation of an evidential network for data fusion in the framework of medical remote monitoring.

This paper presents a medical remote monitoring application which aims at detecting falls. The detection system is based on three modalities: a wearable sensor, infrared sensors and a sound analysis module. The sound analysis is presented briefly.

The multiscenario multienvironment BioSecure Multimodal Database (BMDB).

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data.

A new phase-correlation-based iris matching for degraded images.

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images.

Incremental HMM training applied to ECG signal analysis.

This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual.

On using the Viterbi path along with HMM likelihood information for online signature verification.

This paper describes a system using two complementary sorts of information issuing from a hidden Markov model (HMM) for online signature verification. At each point of the signature, 25 features are extracted. These features are normalized before training and testing in order to improve the performance of the system.

ECG signal analysis through hidden Markov models.

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG.

The effect of sample size and MLP architecture on Bayesian learning for cancer prognosis--a case study.

In this paper we investigate the independent effects of training sample size and multilayer perceptron (MLP) architecture on Bayesian learning to build prognostic models for metastatic breast cancer. We trained two types of Bayesian neural networks on a data set of 1477 metastatic breast cancer patients followed at the Institut Curie using disjoint training sets of sizes k = 50, 100, 200, 300, and 450. The learning performance as measured by an expected loss appeared independent of the two architectures modelling the log hazard function under either proportional or non proportional hazard assumptions, thus indicating that no other sources of nonlinearity besides interactions are present.

Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model.

The principles underlying the organization and operation of the prefrontal cortex have been addressed by neural network modeling. The involvement of the prefrontal cortex in the temporal organization of behavior can be defined by processing units that switch between two stable states of activity (bistable behavior) in response to synaptic inputs. Long-term representation of programs requiring short-term memory can result from activity-dependent modifications of the synaptic transmission controlling the bistable behavior.