Results 241 to 250 of about 6,547,057 (287)

Attention-enhanced hybrid U-Net for prostate cancer grading and explainability. [PDF]

Sci Rep
Zaheer AN, Farhan M, Min G, Alotaibi FA, Alnfiai MM.   +4 more
europepmc   +1 more source

Convolutional recurrent U-net for cardiac cine MRI reconstruction via effective spatio-temporal feature exploitation. [PDF]

Med Phys
Lyu D, Staring M, van Osch MJP, Doneva M, Lamb HJ, Pezzotti N.   +5 more
europepmc   +1 more source

Deterministic nowcasting of geostationary satellite infrared brightness temperature using 3D U-Net diffusion model. [PDF]

Sci Rep
Afzali Gorooh V, Delle Monache L, Axisa D, Sengupta A, Zhang Z, Ralph FM.   +5 more
europepmc   +1 more source

Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction. [PDF]

Med Sci (Basel)
Chirica C, Dobrovăț BI, Chirica SI, Onicescu OM, Rotundu A, Marciuc EA, Cucu LE, Pomohaci D, Anghel RC, Popescu MR, Maștaleru A, Haba D, Leon MM.   +12 more
europepmc   +1 more source

Enhancing U-Net for image denoising with bilateral filter noise residue and gradient estimation (BIRUNet). [PDF]

Sci Rep
Soniya S, Sriharipriya KC, Clement JC, Subramaniam U.   +3 more
europepmc   +1 more source

Deep Learning Analysis of Crystallization Using Polarized Light Microscopy and U-Net Segmentation. [PDF]

J Phys Chem B
Osiecka-Drewniak N, Galewski Z, Piwowarczyk M, Juszyńska-Gałązka E.   +3 more
europepmc   +1 more source

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be ...
Özgün Çiçek, A. Abdulkadir, S. Lienkamp, T. Brox, O. Ronneberger   +4 more
semanticscholar   +3 more sources

U-Net: Convolutional Networks for Biomedical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available ...
O. Ronneberger, P. Fischer, T. Brox
semanticscholar   +3 more sources

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CA-U-Net: Context Aware U-Net For Brain Tumor Segmentation

2025 International Joint Conference on Neural Networks (IJCNN)
Brain tumor is a fatal disease that requires early diagnosis to increase the chances of survival of the patient. However, detection of brain tumors is often a challenging task for doctors and is also a time-consuming process.
Krishen Jagani, Muhammad Abdullah Hanif, J. Angel Arul Jothi, Muhammad Shafique   +3 more
openaire   +2 more sources