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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Cell Segmentation on DIC-HeLa
CELL SEGMENTATION COLORECTAL GLAND SEGMENTATION: ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
Ranked #5 on Breast Tumour Classification on PCam
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Ranked #1 on Breast Tumour Classification on PCam
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Ranked #4 on Breast Tumour Classification on PCam
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Ranked #5 on Multi-tissue Nucleus Segmentation on Kumar
A multilevel random forest technique in a hierarchical way is proposed.