Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
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Segmentation of rodent brain lesions on magnetic resonance images (MRIs) is a time-consuming task with high inter- and intra-operator variability due to its subjective nature.
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Prior art is insufficient due to three challenges, that is, lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes.
In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results.
SOTA for Skin Cancer Segmentation on PH2
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care.
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
This function adds a weighted focal coefficient and combines two traditional loss functions.
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.
Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks.