activity
20182020
most citedSelf-Supervised Nuclei Segmentation in Histopathological Images Using Attention

46 citations · 46 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV202046 cited

Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention

Mihir Sahasrabudhe, Stergios Christodoulidis, Roberto Salgado +5

Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These meth…

cs.CG2019

Proof of Correctness and Time Complexity Analysis of a Maximum Distance Transform Algorithm

Mihir Sahasrabudhe, Siddhartha Chandra

The distance transform algorithm is popular in computer vision and machine learning domains. It is used to minimize quadratic functions over a grid of points. Felzenszwalb and Hutt…

cs.CV2019

Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion

Mihir Sahasrabudhe, Zhixin Shu, Edward Bartrum +3

In this work we introduce Lifting Autoencoders, a generative 3D surface-based model of object categories. We bring together ideas from non-rigid structure from motion, image format…

cs.CV2018

Linear and Deformable Image Registration with 3D Convolutional Neural Networks

Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou +4

Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a n…

cs.CV2018

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

Zhixin Shu, Mihir Sahasrabudhe, Alp Guler +3

In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template par…