5 papers
Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder
Juexin Zhang, Qifeng Zhong, Ying Weng +1
The significant molecular and pathological heterogeneity of glioblastoma, an aggressive brain tumor, complicates diagnosis and patient stratification. While traditional histopathol…
Robust 3D Brain MRI Inpainting with Random Masking Augmentation
Juexin Zhang, Ying Weng, Ke Chen
The ASNR-MICCAI BraTS-Inpainting Challenge was established to mitigate dataset biases that limit deep learning models in the quantitative analysis of brain tumor MRI. This paper de…
Deep Learning for Glioblastoma Morpho-pathological Features Identification: A BraTS-Pathology Challenge Solution
Juexin Zhang, Ying Weng, Ke Chen
Glioblastoma, a highly aggressive brain tumor with diverse molecular and pathological features, poses a diagnostic challenge due to its heterogeneity. Accurate diagnosis and assess…
U-Net Based Healthy 3D Brain Tissue Inpainting
Juexin Zhang, Ying Weng, Ke Chen
This paper introduces a novel approach to synthesize healthy 3D brain tissue from masked input images, specifically focusing on the task of 'ASNR-MICCAI BraTS Local Synthesis of Ti…
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Florian Kofler, Felix Meissen, Felix Steinbauer +103
A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…