Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
[HTML][HTML] Deep learning in medical imaging
The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by
the human brain system, was developed by connecting layers with artificial neurons …
the human brain system, was developed by connecting layers with artificial neurons …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Unet++: Redesigning skip connections to exploit multiscale features in image segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …
Transmed: Transformers advance multi-modal medical image classification
Y Dai, Y Gao, F Liu - Diagnostics, 2021 - mdpi.com
Over the past decade, convolutional neural networks (CNN) have shown very competitive
performance in medical image analysis tasks, such as disease classification, tumor …
performance in medical image analysis tasks, such as disease classification, tumor …
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
[HTML][HTML] Unsupervised brain imaging 3D anomaly detection and segmentation with transformers
Pathological brain appearances may be so heterogeneous as to be intelligible only as
anomalies, defined by their deviation from normality rather than any specific set of …
anomalies, defined by their deviation from normality rather than any specific set of …
State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images
Brain tumors have become a leading cause of death around the globe. The main reason for
this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately …
this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately …
A medical image segmentation method based on multi-dimensional statistical features
Y Xu, X He, G Xu, G Qi, K Yu, L Yin, P Yang… - Frontiers in …, 2022 - frontiersin.org
Medical image segmentation has important auxiliary significance for clinical diagnosis and
treatment. Most of existing medical image segmentation solutions adopt convolutional …
treatment. Most of existing medical image segmentation solutions adopt convolutional …
Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to mapping the structure and function of the brain …
offer a non-invasive approach to mapping the structure and function of the brain …