Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
Medical image segmentation review: The success of u-net
Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray
Background and objective Automatic segmentation and annotation of medical image plays a
critical role in scientific research and the medical care community. Automatic segmentation …
critical role in scientific research and the medical care community. Automatic segmentation …
Channel prior convolutional attention for medical image segmentation
H Huang, Z Chen, Y Zou, M Lu, C Chen, Y Song… - Computers in Biology …, 2024 - Elsevier
Characteristics such as low contrast and significant organ shape variations are often
exhibited in medical images. The improvement of segmentation performance in medical …
exhibited in medical images. The improvement of segmentation performance in medical …
A unified framework for U-Net design and analysis
C Williams, F Falck, G Deligiannidis… - Advances in …, 2024 - proceedings.neurips.cc
U-Nets are a go-to neural architecture across numerous tasks for continuous signals on a
square such as images and Partial Differential Equations (PDE), however their design and …
square such as images and Partial Differential Equations (PDE), however their design and …
[HTML][HTML] Cellvit: Vision transformers for precise cell segmentation and classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …
are important clinical tasks and crucial for a wide range of applications. However, it is a …
[HTML][HTML] Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning
B Shi, M Patel, D Yu, J Yan, Z Li, D Petriw… - Science of The Total …, 2022 - Elsevier
Microplastics quantification and classification are demanding jobs to monitor microplastic
pollution and evaluate the potential health risks. In this paper, microplastics from daily …
pollution and evaluate the potential health risks. In this paper, microplastics from daily …
[HTML][HTML] Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans
Background and Motivation: COVID-19 has resulted in a massive loss of life during the last
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
Hierarchical attention feature fusion-based network for land cover change detection with homogeneous and heterogeneous remote sensing images
Deep learning techniques have become popular in land cover change detection (LCCD)
with remote sensing images (RSIs). However, many existing networks mostly concentrate on …
with remote sensing images (RSIs). However, many existing networks mostly concentrate on …