1 code implementation • 8 Dec 2023 • Reza Azad, Moein Heidary, Kadir Yilmaz, Michael Hüttemann, Sanaz Karimijafarbigloo, Yuli Wu, Anke Schmeink, Dorit Merhof
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems.
1 code implementation • 22 Nov 2023 • Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
no code implementations • 21 Nov 2023 • Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
1 code implementation • 31 Aug 2023 • Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Yury Velichko, Ulas Bagci, Dorit Merhof
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
1 code implementation • 26 Jul 2023 • Sanaz Karimijafarbigloo, Reza Azad, Dorit Merhof
This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data.
Ranked #16 on Few-Shot Semantic Segmentation on FSS-1000 (5-shot)
1 code implementation • 27 Nov 2022 • Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, Dorit Merhof
U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities.
1 code implementation • 1 Aug 2022 • Reza Azad, Moein Heidari, Moein Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, Ehsan Adeli, Dorit Merhof
Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks.