Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
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We argue that a boundary loss can mitigate the difficulties of regional losses in the context of highly unbalanced segmentation problems because it uses integrals over the boundary between regions instead of unbalanced integrals over regions.
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps.
SOTA for Lesion Segmentation on ISIC 2018
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
SOTA for Lung Nodule Segmentation on LUNA
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.
We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout  in the context of deep networks for lesion detection and segmentation in medical images.
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models.
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
#2 best model for Skin Cancer Segmentation on Kaggle Skin Lesion Segmentation