Mar 15, 2023 · A semantic segmentation network based on 2D images with multi encoder structure for multi-modal MRI tumor sub-region segmentation.
Overall, the main contributions of our work are as follows: first, we propose the MEHLC network, which combines multi-encoders with hybrid lateral connections.
This paper presents a new hybrid method which integrates multiscale analysis, image normalization and elastic template deformation. Read more. Conference Paper ...
This paper introduces a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images and ...
In this paper, we propose a multi-modal brain tumor segmentation framework that adopts the hybrid fusion of modality-specific features using ...
In this paper, we give an overview of deep learning-based approaches for multi-modal medical image segmentation task. Firstly, we introduce the general ...
Multi-modal Brain Image Segmentation Based on Multi-Encoder with Hybrid Lateral Connection (MEHLC-Net) · Computer Science, Medicine. ICBBE · 2022.
This paper describes a convolutional neural network called F-S-Net that fuses the information from multimodal medical images and uses the semantic information ...
Missing: Hybrid (MEHLC-
Mar 21, 2022 · We propose a model for brain tumor segmentation with multiple encoders. The structure contains four encoders and one decoder.
Missing: Hybrid Lateral (MEHLC-
Multi-modal Brain Image Segmentation Based on Multi-Encoder with Hybrid Lateral Connection (MEHLC-Net). ... Based on Adaptive Feature Fusion of Brain ...