A survey of deep active learning
Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …
Heterogeneity of red blood cells: causes and consequences
A Bogdanova, L Kaestner, G Simionato… - Frontiers in …, 2020 - frontiersin.org
Mean values of hematological parameters are currently used in the clinical laboratory
settings to characterize red blood cell properties. Those include red blood cell indices …
settings to characterize red blood cell properties. Those include red blood cell indices …
Robust asymmetric loss for multi-label long-tailed learning
In real medical data, training samples typically show long-tailed distributions with multiple
labels. Class distribution of the medical data has a long-tailed shape, in which the incidence …
labels. Class distribution of the medical data has a long-tailed shape, in which the incidence …
[HTML][HTML] Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …
continuously attracted the research community's interest, motivated by their promising …
DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification
Abstract Acute Lymphoblastic Leukemia is one of the fatal types of disease which causes a
high mortality rate among children and adults. Traditional diagnosing of this disease is …
high mortality rate among children and adults. Traditional diagnosing of this disease is …
Attention based multiple instance learning for classification of blood cell disorders
Red blood cells are highly deformable and present in various shapes. In blood cell
disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis …
disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis …
RedTell: an AI tool for interpretable analysis of red blood cell morphology
Introduction: Hematologists analyze microscopic images of red blood cells to study their
morphology and functionality, detect disorders and search for drugs. However, accurate …
morphology and functionality, detect disorders and search for drugs. However, accurate …
A continual learning approach for cross-domain white blood cell classification
Accurate classification of white blood cells in peripheral blood is essential for diagnosing
hematological diseases. Due to constantly evolving clinical settings, data sources, and …
hematological diseases. Due to constantly evolving clinical settings, data sources, and …
Computer vision quantitation of erythrocyte shape abnormalities provides diagnostic, prognostic, and mechanistic insight
Examination of red blood cell (RBC) morphology in peripheral blood smears can help
diagnose hematologic diseases, even in resource-limited settings, but this analysis remains …
diagnose hematologic diseases, even in resource-limited settings, but this analysis remains …
A comprehensive survey on deep active learning in medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …