IEEE

SDCnet: A Deep Learning Network Model for Road Segmentation Based on CNN and Transformer

Abstract: Road segmentation is a key task in remote sensing semantic segmentation, and the existing deep learning methods still have the problems of insufficient fineness, difficulty in modeling ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
IEEE

Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography

Abstract: The automatic segmentation of liver tumors plays an important role in the diagnosis and treatment of liver cancer. While deep convolutional neural network (DCNN) models are widely used for ...
Frontiers

A deep learning aided bone marrow segmentation of quantitative fat MRI for myelofibrosis patients

1 Department of Radiology, University of Michigan, Ann Arbor, MI, United States 2 Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI, United States Purpose: To ...
Frontiers

Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model

Objective: Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to ...
PNAS

OMT and tensor SVD–based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study

Accurate characterization of glioma is essential for effective clinical decision-making. Most current studies involve a limited number of patients and focus solely on single-gene tasks. This research ...