Multiple-instance learning for medical image and video analysis
G Quellec, G Cazuguel, B Cochener… - IEEE reviews in …, 2017 - ieeexplore.ieee.org
Multiple-instance learning (MIL) is a recent machine-learning paradigm that is particularly
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …
Personalized medicine for patients with COPD: where are we?
FME Franssen, P Alter, N Bar… - … journal of chronic …, 2019 - Taylor & Francis
Chronic airflow limitation is the common denominator of patients with chronic obstructive
pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of …
pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of …
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Background Growing concerns about increasing rates of antibiotic resistance call for
expanded and comprehensive global monitoring. Advancing methods for monitoring of …
expanded and comprehensive global monitoring. Advancing methods for monitoring of …
Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features
Necessary screenings must be performed to control the spread of the COVID‐19 in daily life
and to make a preliminary diagnosis of suspicious cases. The long duration of pathological …
and to make a preliminary diagnosis of suspicious cases. The long duration of pathological …
Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
RF Mansour, J Escorcia-Gutierrez, M Gamarra… - Pattern Recognition …, 2021 - Elsevier
At present times, COVID-19 has become a global illness and infected people has increased
exponentially and it is difficult to control due to the non-availability of large quantity of testing …
exponentially and it is difficult to control due to the non-availability of large quantity of testing …
An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19
DA Pustokhin, IV Pustokhina, PN Dinh… - Journal of Applied …, 2023 - Taylor & Francis
In recent days, COVID-19 pandemic has affected several people's lives globally and
necessitates a massive number of screening tests to detect the existence of the coronavirus …
necessitates a massive number of screening tests to detect the existence of the coronavirus …
Dissimilarity-based representations for one-class classification on time series
In several real-world classification problems it can be impractical to collect samples from
classes other than the one of interest, hence the need for classifiers trained on a single …
classes other than the one of interest, hence the need for classifiers trained on a single …
Classification of COPD with multiple instance learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection
benefits the survival rate. COPD can be quantified by classifying patches of computed …
benefits the survival rate. COPD can be quantified by classifying patches of computed …
Metagenomic insights into the antibiotic resistome of mangrove sediments and their association to socioeconomic status
M Imchen, R Kumavath - Environmental Pollution, 2021 - Elsevier
Mangrove sediments are prone to anthropogenic activities that could enrich antibiotics
resistance genes (ARGs). The emergence and dissemination of ARGs are of serious …
resistance genes (ARGs). The emergence and dissemination of ARGs are of serious …
A dissimilarity-based imbalance data classification algorithm
Class imbalances have been reported to compromise the performance of most standard
classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this …
classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this …