Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
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A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training.
The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images.
To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning.
Technology aided platforms provide reliable tools in almost every field these days.
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue.
Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma.
In this paper we present algorithms to diagnosis Pathological Myopia (PM) and detection of retinal structures and lesions such asOptic Disc (OD), Fovea, Atrophy and Detachment.
A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.
Domain adaptation in healthcare data is a potentially critical component in making computer-aided diagnostic systems applicable cross multiple sites and imaging scanners.
In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.