Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
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We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
SOTA for Lesion Segmentation on ISLES-2015
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Lung Nodule Segmentation on LUNA
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge.
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.
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.