Výsledky vyhledávání - "Chilamkurthy, Sasank"

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C-lisp and Flexible Macro Programming with S-expressions

Autor: Padmaraman, Vedanth, Chilamkurthy, Sasank

Llama$.$lisp is a compiler framework intended to target offload processor backends such as GPUs, using intermediate representation languages (IRs) that are device-agnostic. The Llama$.$lisp IRs are formulated as S-expressions. This makes them easy to

Externí odkaz: http://arxiv.org/abs/2410.16690

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Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement

Autor: Upadhyay, Ujjwal, Ranjan, Mukul, Golla, Satish, Tanamala, Swetha, Sreenivas, Preetham, Chilamkurthy, Sasank, Pandian, Jeyaraj, Tarpley, Jason

A stroke occurs when an artery in the brain ruptures and bleeds or when the blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's tissues due to the rupture or obstruction resulting in tissue death. The Middle cerebral arter

Externí odkaz: http://arxiv.org/abs/2203.03622

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that suppor

Externí odkaz: http://arxiv.org/abs/1912.01703

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Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

Autor: Chilamkurthy, Sasank, Ghosh, Rohit, Tanamala, Swetha, Biviji, Mustafa, Campeau, Norbert G., Venugopal, Vasantha Kumar, Mahajan, Vidur, Rao, Pooja, Warier, Prashant

Importance: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. Objective: To develop and validate a set of deep learning algorithms for automated detection of following key findings

Externí odkaz: http://arxiv.org/abs/1803.05854

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2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

Autor: Patravali, Jay, Jain, Shubham, Chilamkurthy, Sasank

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset compris

Externí odkaz: http://arxiv.org/abs/1707.09813

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