Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
Reversible vision transformers
Abstract We present Reversible Vision Transformers, a memory efficient architecture design
for visual recognition. By decoupling the GPU memory footprint from the depth of the model …
for visual recognition. By decoupling the GPU memory footprint from the depth of the model …
Embryosformer: Deformable transformer and collaborative encoding-decoding for embryos stage development classification
The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a
key predictor of embryo viability. However, observing cell divisions in Time-Lapse …
key predictor of embryo viability. However, observing cell divisions in Time-Lapse …
Random fourier features-based deep learning improvement with class activation interpretability for nerve structure segmentation
CA Jimenez-Castaño, AM Álvarez-Meza… - Sensors, 2021 - mdpi.com
Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia.
Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby …
Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby …
[HTML][HTML] On the approximation of bi-Lipschitz maps by invertible neural networks
Invertible neural networks (INNs) represent an important class of deep neural network
architectures that have been widely used in applications. The universal approximation …
architectures that have been widely used in applications. The universal approximation …
MSAIF-Net: A Multi-Stage Spatial Attention based Invertible Fusion Network for MR Images
X Zhang, A Liu, P Jiang, R Qian… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, multimodal medical image fusion has drawn increasing attention, aiming to
provide comprehensive information for image understanding and clinical applications. With …
provide comprehensive information for image understanding and clinical applications. With …
Dam-al: Dilated attention mechanism with attention loss for 3d infant brain image segmentation
While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain
analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter …
analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter …
3D AttU-NET for brain tumor segmentation with a novel loss function
In the United States of America (USA), every year 150,000 patients are registered with a
secondary brain tumor that is not generated in the brain. This necessitates the need for early …
secondary brain tumor that is not generated in the brain. This necessitates the need for early …
Saresu-net: Shuffle attention residual u-net for brain tumor segmentation
Y Zhang, Y Han, D Liu, J Zhang - 2022 15th International …, 2022 - ieeexplore.ieee.org
Computer-aided segmentation technology is important for clinical treatment of brain tumors.
In recent years, U-shaped networks have become mainstream for medical image …
In recent years, U-shaped networks have become mainstream for medical image …
A Review of Recent Advancements in Infant Brain MRI Segmentation Using Deep Learning Approaches
In this paper, a critical analysis of recent trends and techniques for tissue segmentation of an
pediatric brain Magnetic Resonance Imaging (MRI) is performed. A significant amount of …
pediatric brain Magnetic Resonance Imaging (MRI) is performed. A significant amount of …