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Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
#3 best model for Crowd Counting on UCF-QNRF
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
#6 best model for Breast Tumour Classification on PCam
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
SOTA for 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.
#5 best model for Multi-tissue Nucleus Segmentation on Kumar
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
3D MEDICAL IMAGING SEGMENTATION AUTOMATIC MACHINE LEARNING MODEL SELECTION BREAST CANCER DETECTION BREAST MASS SEGMENTATION IN WHOLE MAMMOGRAMS BREAST TUMOUR CLASSIFICATION INTERPRETABLE MACHINE LEARNING MATHEMATICAL PROOFS MEDICAL DIAGNOSIS MEDICAL IMAGE RETRIEVAL PROBABILISTIC DEEP LEARNING