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Diffusion models in medical imaging: A comprehensive survey
A Kazerouni, EK Aghdam, M Heidari, R Azad… - Medical image …, 2023 - Elsevier
Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
ETM Beltrán, MQ Pérez, PMS Sánchez… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Segment anything model for medical image analysis: an experimental study
MA Mazurowski, H Dong, H Gu, J Yang, N Konz… - Medical Image …, 2023 - Elsevier
Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …
Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
Medical sam adapter: Adapting segment anything model for medical image segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
nnFormer: volumetric medical image segmentation via a 3D transformer
Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …
from the medical imaging community. Given the ability to exploit long-term dependencies …
Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
The Transformer architecture has demonstrated remarkable results in 3D medical image
segmentation due to its capability of modeling global relationships. However, it poses a …
segmentation due to its capability of modeling global relationships. However, it poses a …
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 …
Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
A comprehensive survey on applications of transformers for deep learning tasks
S Islam, H Elmekki, A Elsebai, J Bentahar… - Expert Systems with …, 2024 - Elsevier
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …