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20222026
most citedThe 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

18 citations · 55 across the 15 of their papers we have counts for

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8 papers · 1 filter

eess.IV2024★ 3 cited

Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients

Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast +5

Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase…

cs.CV2024★ 18 cited

The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Maria Correia de Verdier, Rachit Saluja, Louis Gagnon +82

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the gene…

cs.CV2024★ 1 cited

BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions

Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani +19

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is wide…

cs.CV2024★ 7 cited

Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge

Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…

eess.IV2024★ 9 cited

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…

eess.IV2024★ 1 cited

RadRotator: 3D Rotation of Radiographs with Diffusion Models

Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani +4

Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few…