Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The proton exchange membrane fuel cell (PEMFC) is an important technology for clean power generation in a decarbonized hydrogen system and is notably envisioned for applications in heavy-duty transportation. However, for cost and performance competitivenes ...

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Infectious diseases continue to pose a substantial burden on global populations, requiring innovative broad-spectrum prophylactic and treatment alternatives. Here, we have designed modular synthetic polymer nanopartides that mimic functional components of ...

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring system. However, the covariate shift between RSI datasets under different capture conditions cannot be alleviated by directly using the unsupervised domain adaptatio ...

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied ...

In this paper, we study an emerging class of neural networks, the Morphological Neural networks, from some modern perspectives. Our approach utilizes ideas from tropical geometry and mathematical morphology. First, we state the training of a binary morphol ...

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

Computational studies of metabolism aim to systematically analyze the metabolic behaviour of biological systems in different conditions. Genome-scale metabolic network models (GEMs) capture the connection between elements of the network by applying stoichi ...

Background Low-density (LD)Plasmodiuminfections are missed by standard malaria rapid diagnostic tests (standard mRDT) when the blood antigen concentration is below the detection threshold. The clinical impact of these LD infections is unknown. This study i ...

Near infrared optical tomography (NIROT) is an emerging modality that enables imaging the oxygenation of tissue, which is a biomarker of tremendous clinical relevance. Measuring in reflectance is usually required when NIROT is applied in clinical scenarios ...

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing ...

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...

This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two ...

Despite an unprecedented 2 decades of success, the combat against malaria - the mosquito-transmitted disease caused by Plasmodium parasites - is no longer progressing. Efforts toward eradication are threatened by the lack of an effective vaccine and a rise ...

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Chattez avec Graph Search

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Aggregating Spatial and Photometric Context for Photometric Stereo

Multidimensional Electron Imaging of Catalyst Layer Materials

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations

Detecting Anomalies and Obstacles in Road Scenes

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Potent Virustatic Polymer-Lipid Nanomimics Block Viral Entry and Inhibit Malaria Parasites In Vivo

Attribute Prediction as Multiple Instance Learning

A Stepwise Domain Adaptive Segmentation Network With Covariate Shift Alleviation for Remote Sensing Imagery

Counting People by Estimating People Flows

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Human-Centered Scene Understanding via Crowd Counting

Optimization methods and objective functions for analysis of metabolic network models

Clinical relevance of low-densityPlasmodium falciparumparasitemia in untreated febrile children: A cohort study

Image reconstruction for novel time domain near infrared optical tomography: towards clinical applications

HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison

Targeting liver stage malaria with metformin