Recent progress in transformer-based medical image analysis

Z Liu, Q Lv, Z Yang, Y Li, CH Lee, L Shen - Computers in Biology and …, 2023 - Elsevier
The transformer is primarily used in the field of natural language processing. Recently, it has
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …

[HTML][HTML] Vision transformers in multi-modal brain tumor MRI segmentation: A review

P Wang, Q Yang, Z He, Y Yuan - Meta-Radiology, 2023 - Elsevier
Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …

CKD-TransBTS: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation

J Lin, J Lin, C Lu, H Chen, H Lin, B Zhao… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain
tumor diagnosis, cancer management and research purposes. With the great success of the …

Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation

Z Zhu, M Sun, G Qi, Y Li, X Gao, Y Liu - Computers in Biology and Medicine, 2024 - Elsevier
Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation

W Li, W Huang, Y Zheng - IEEE Journal of Biomedical and …, 2024 - ieeexplore.ieee.org
Accurate segmentation of brain tumors in MRI images is imperative for precise clinical
diagnosis and treatment. However, existing medical image segmentation methods exhibit …

Sketch-supervised histopathology tumour segmentation: Dual CNN-transformer with global normalised CAM

Y Li, L Wang, X Huang, Y Wang, L Dong… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Deep learning methods are frequently used in segmenting histopathology images with high-
quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like …

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

FA Mohammed, KK Tune, BG Assefa, M Jett… - Machine Learning and …, 2024 - mdpi.com
In this review, we compiled convolutional neural network (CNN) methods which have the
potential to automate the manual, costly and error-prone processing of medical images. We …

HEA-Net: attention and MLP hybrid encoder architecture for medical image segmentation

L An, L Wang, Y Li - Sensors, 2022 - mdpi.com
The model, Transformer, is known to rely on a self-attention mechanism to model distant
dependencies, which focuses on modeling the dependencies of the global elements …

M FTrans: Modality-Masked Fusion Transformer for Incomplete Multi-Modality Brain Tumor Segmentation

J Shi, L Yu, Q Cheng, X Yang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Brain tumor segmentation is a fundamental task and existing approaches usually rely on
multi-modality magnetic resonance imaging (MRI) images for accurate segmentation …

mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI

P Li, Z Li, Z Wang, C Li, M Wang - Medical & Biological Engineering & …, 2024 - Springer
Brain tumor segmentation is an important direction in medical image processing, and its
main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand …