A review on traditional machine learning and deep learning models for WBCs classification in blood smear images
In computer vision, traditional machine learning (TML) and deep learning (DL) methods
have significantly contributed to the advancements of medical image analysis (MIA) by …
have significantly contributed to the advancements of medical image analysis (MIA) by …
Applying self-supervised learning to medicine: review of the state of the art and medical implementations
A Chowdhury, J Rosenthal, J Waring, R Umeton - Informatics, 2021 - mdpi.com
Machine learning has become an increasingly ubiquitous technology, as big data continues
to inform and influence everyday life and decision-making. Currently, in medicine and …
to inform and influence everyday life and decision-making. Currently, in medicine and …
Universeg: Universal medical image segmentation
While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
Panda: Adapting pretrained features for anomaly detection and segmentation
Anomaly detection methods require high-quality features. In recent years, the anomaly
detection community has attempted to obtain better features using advances in deep self …
detection community has attempted to obtain better features using advances in deep self …
Scribbleprompt: fast and flexible interactive segmentation for any biomedical image
HE Wong, M Rakic, J Guttag, AV Dalca - European Conference on …, 2024 - Springer
Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …
With enough labelled data, deep learning models can be trained to accurately automate …
Deep nearest neighbor anomaly detection
Nearest neighbors is a successful and long-standing technique for anomaly detection.
Significant progress has been recently achieved by self-supervised deep methods (eg …
Significant progress has been recently achieved by self-supervised deep methods (eg …
Recognition of peripheral blood cell images using convolutional neural networks
Background and objectives Morphological analysis is the starting point for the diagnostic
approach of more than 80% of hematological diseases. However, the morphological …
approach of more than 80% of hematological diseases. However, the morphological …
Medical sam 2: Segment medical images as video via segment anything model 2
J Zhu, Y Qi, J Wu - arXiv preprint arXiv:2408.00874, 2024 - arxiv.org
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment
planning, yet existing models often face challenges in generalization and in handling both …
planning, yet existing models often face challenges in generalization and in handling both …
WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet
The counting and identification of white blood cells (WBCs, ie, leukocytes) in blood smear
images play a crucial role in the diagnosis of certain diseases, including leukemia …
images play a crucial role in the diagnosis of certain diseases, including leukemia …
An automatic nucleus segmentation and CNN model based classification method of white blood cell
White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose
blood-related diseases, pathologists need to consider the characteristics of WBC. The …
blood-related diseases, pathologists need to consider the characteristics of WBC. The …