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Found 7 papers, 2 papers with code
Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation
Medical image segmentation using deep neural networks has been highly successful.
Data-Driven Deep Supervision for Skin Lesion Classification
Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.
Data-Driven Deep Supervision for Medical Image Segmentation
In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction.
Image Complexity Guided Network Compression for Biomedical Image Segmentation
Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network.
Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation
In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation.
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations.
