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Found 4 papers, 1 papers with code
MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation
Recently, training 3D fault segmentation using sparse manual 2D slices is thought to yield promising results, but manual labeling has many false negative labels (abnormal annotations), which is detrimental to training and consequently to detection performance.
Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels
The task of image segmentation requires huge labels, especially 3D seismic data, which has a complex structure and lots of noise.
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients.
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images
Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task.
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