activity
20182022
most citedUNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

76 citations · 85 across the 6 of their papers we have counts for

collaborators

9 papers

eess.IV20222 cited

Automated head and neck tumor segmentation from 3D PET/CT

Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang +2

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET…

eess.IV20222 cited

Automated segmentation of intracranial hemorrhages from 3D CT

Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He +2

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs…

eess.IV20223 cited

Automated ischemic stroke lesion segmentation from 3D MRI

Md Mahfuzur Rahman Siddique, Dong Yang, Yufan He +2

Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs.…

eess.IV2022

HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu +3

Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies…

eess.IV20212 cited

Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs

Md Mahfuzur Rahman Siddiquee, Andriy Myronenko

Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation…

eess.IV202076 cited

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh +1

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (…