Aymeric Histace

École Nationale Supérieure de l'Electronique et de ses Applications | ENSEA · Information Processing Departement

Introduction Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and cl...

Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that sph...

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study in...

Polyp segmentation within colonoscopy video frames using deep learning models has the potential to automate colonoscopy screening procedures. This could help improve the early lesion detection rate and in vivo characterization of polyps which could develop into colorectal cancer. Recent state-of-the-art deep learning polyp segmentation models have...

Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we ha...

Insects that spread diseases like malaria, chikungunya and Lyme disease are found all over the world because of climate change, economic fluctuations, human migration, and international trade. In this study, we propose DeepSquitoes, a mobile system framework for insect identification and fast data dissemination, with the goal of improving the manag...

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field applicati...

To characterize the growth of brain organoids (BOs), cultures that replicate some early physiological or pathological developments of the human brain are usually manually extracted. Due to their novelty, only small datasets of these images are available, but segmenting the organoid shape automatically with deep learning (DL) tools requires a larger...

We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten...

Introduction Datasets containing only few images are common in the biomedical field. This poses a global challenge for the development of robust deep-learning analysis tools, which require a large number of images. Generative Adversarial Networks (GANs) are an increasingly used solution to expand small datasets, specifically in the biomedical domai...

Background and study aim: Pan-enteric capsule endoscopy (PCE) is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence (AI) algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and stan...

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field applicati...

Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues,...

We propose to use Topological Data Analysis (TDA) to characterize the morphological development of brain organoids. We combine TDA with clustering strategies to characterize the morphology of three developmental stages of segmented brain organoid images. We calculate a linear regression of the H1 feature diagrams as well as entropy, dispersion, and...

A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-tra...

Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings. However current AD models still struggle on edge-case normal samples and are often unable to keep high performance over different scales of anomalies. Moreover, there currently does not exist a unified framework efficiently covering b...

Background Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives In the current paper, we aimed...

Capsule endoscopy (CE) is a valid alternative to conventional gastrointestinal (GI) endoscopy tools. In CE, annotation tools are crucial in developing large and annotated medical image databases for training deep neural networks (DNN). We provide an overview of the described and in-use various annotation systems available, focusing on the annotatio...

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (P...

Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an aud...

Deep anomaly detection has proven to be an efficient and robust approach in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack fine...

Background and aims: Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic). Material and m...

As said before, WCE has rapidly become the standard minimally invasive method for visualization of the Small Bowel (SB) which is highly difficult to reach using classic endoscopy techniques like enteroscopy.

The objective of this book was to present several methodologies that have been proposed during the last few years aiming to assist clinicians in some of the most demanding gastrointestinal endoscopy tasks. More precisely, we focus on the analysis of those methods targeting polyp characterization (detection and segmentation) in colonoscopy images an...

The Computer-Assisted Diagnosis for Capsule Endoscopy Database (CAD-CAP) is a French national multicenter database approved by the French Data Protection Authority in which still frames (associated with short adjacent video sequences) collected from 4,166 deidentified, third-generation SB-VCE videos (Pillcam SB3 system, Medtronic) routinely recorde...

Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids. Methods: To...

This book opens with an introduction to the main purpose and tasks of the GIANA challenge, as well as a summary and an analysis of the results and performance obtained by the 20 participating teams. The early and accurate diagnosis of gastrointestinal diseases is critical for increasing the chances of patient survival, and efficient screening is vi...

Wireless Capsule Endoscopy (WCE) takes the form of a pill equipped with a CCD or CMOS sensor, two batteries, and a RF (radiofrequency) transmitter that enables the wireless identification of gastrointestinal abnormalities such as ulcers, blood, and polyps (Moglia et al. 2009) with no need for hospitalization or sedation.

As explained in previous chapters, in 2017 and 2018, an evolution of the tasks of GIANA challenge related to WCE was proposed. More precisely, in 2017, it was the first time that a task focusing on lesion detection for WCE was proposed in the context of an international challenge, and a particular focus was done on vascular lesion detection. Conseq...

We present in this section some works belonging to teams that also took part in the different editions of GIANA challenge but, for time reasons, were not able to provide full chapters like the rest of teams.

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not pe...

Bio-inspired Event-Based (EB) cameras are a promising new technology that outperforms standard frame-based cameras in extreme lighted and fast moving scenes. Already, a number of EB corner detection techniques have been developed; however, the performance of these EB corner detectors has only been evaluated based on a few author-selected criteria r...

Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarc...

Background Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscop...

Background and aims: Cleanliness scores in small bowel (SB) capsule endoscopy (CE) have poor reproducibility. The aim of this study was to evaluate a neural network (NN)-based algorithm for automated assessment of the SB cleanliness during CE. Methods: First, 600 normal third-generation SBCE still frames were categorized as "adequate" or "inadeq...

In deep metric learning, the training procedure relies on sampling informative tuples. However, as the training procedure progresses, it becomes nearly impossible to sample relevant hard negative examples without proper mining strategies or generation-based methods. Recent work on hard negative generation have shown great promises to solve the mini...

Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on impr...

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). Th...

Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on impr...

Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarc...

Fibrosis represents an open issue for medium to long-term active implants, such as pacemakers, given that this biological medium surrounds the stimulation electrodes and can impact or modify the perfor- mances of the system. For this reason, Embedded Impedance Spectroscopy (EIS) techniques have been investigated these last years to sense the fibros...

Learning rich and compact representations is an open topic in many fields such as object recognition or image retrieval. Deep neural networks have made a major breakthrough during the last few years for these tasks but their representations are not necessary as rich as needed nor as compact as expected. To build richer representations, high-order s...

Learning an effective similarity measure between image representations is key to the success of recent advances in visual search tasks (e.g. verification or zero-shot learning). Although the metric learning part is well addressed, this metric is usually computed over the average of the extracted deep features. This representation is then trained to...

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. Its diagnostic yield can be reduced by poor mucosal visualization. We aimed to evaluate three electronic parameters – colorimetry, abundance of bubbles, and brightness – to assess the adequacy of mucosal visualization of SB-CE images. Patients...

Retinal blood vessels segmentation is an important step for computer-aided early diagnosis of several retinal vascular diseases, in particular diabetic retinopathy. This segmentation is necessary to evaluate the state of the vascular network and to detect abnormalities (aneurysms, hemorrhages, etc). Many image processing and machine learning method...

Learning rich and compact representations is an open topic in many fields such as object recognition or image retrieval. Deep neural networks have made a major breakthrough during the last few years for these tasks but their representations are not necessary as rich as needed nor as compact as expected. To build richer representations, high order s...

Fibrosis represents an open issue for mid- to long-term active implants, like pacemakers, given that this biological tissue surrounds the stimulation electrodes and can impact or modify the performances of the system. For this reason, we present a strategy for the continuous sensing of fibrosis induced by cardiac implants, based on the use of the s...

Artificial intelligence (AI) aims to simulate the human intelligence. It is a cognitive science which relies on neurobiology, logical and critical thinking (problem solving, deep learning, neural networks), computing sciences (calculation, internet), and on databases. Big data exploitation (epidemiology, predictive medicine) and “signals” analysis...

Real-time monitoring of hematophagous diptera (such as mosquitoes) populations in the field is a crucial challenge to foresee vaccination campaigns and to restrain potential diseases spreading. However, current methods heavily rely on costly DNA extraction which is destructive, costly, time consuming and requires experts. The contributions of this...

Purpose: Methodology evaluation for decision support systems for health is a time-consuming task. To assess performance of polyp detection methods in colonoscopy videos, clinicians have to deal with the annotation of thousands of images. Current existing tools could be improved in terms of flexibility and ease of use. Methods: We introduce GTCre...

Background and study aims Colon capsule endoscopy (CCE) does not possess an objective and reliable scoring system to assess the quality of visualization of the colon mucosa. The aim of this study was to establish a colonic computed assessment of cleansing (CAC) score able to discriminate “adequately cleansed” from “inadequately cleansed” CCE still...