Deep learning

(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation

Segmentation plays a crucial role in diagnosis. Studying the retinal vasculatures from fundus images help identify early signs of many crucial illnesses such as diabetic retinopathy. Due to the varying shape, size, and patterns of retinal vessels, …

B-SegNet: branched-SegMentor network for skin lesion segmentation

Melanoma is the most common form of cancer in the world. Early diagnosis of the disease and an accurate estimation of its size and shape are crucial in preventing its spread to other body parts. Manual segmentation of these lesions by a radiologist …

M2SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation(Abstract Presentation)

Segmentation plays a crucial role in diagnosis. Studying the retinal vasculatures from fundus images help identify early signs of many crucial illnesses such as diabetic retinopathy. Due to the varying shape, size, and patterns of retinal vessels, …

Variational AutoEncoder

In deep learning, Variational autoencoders (VAEs) is a technique for learning latent representations. They are also used in a number of semi supervised tasks such as to draw/generate images.

Iris Segmentation in the Wild Using Encoder Decoder Based Deep LearningTechniques

Iris recognition is considered to be one of the most widely used biometric modality, mainly due to its non-invasive nature and high reliability. However, in the whole process of authentication, segmentation of the iris is the most crucial one as …