no code implementations • 18 Jan 2024 • Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R Maiya, Max Ehrlich, Abhinav Shrivastava
We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs which we study learn to ''see'' the frames they represent in surprising ways.
no code implementations • 12 Jan 2019 • Harshita Seth, Pulkit Kumar, Muktabh Mayank Srivastava
Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data.
no code implementations • 18 Oct 2018 • Simmi Mourya, Sonaal Kant, Pulkit Kumar, Anubha Gupta, Ritu Gupta
Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers.
no code implementations • 12 Jun 2018 • Pulkit Kumar, Pravin Nagar, Chetan Arora, Anubha Gupta
Automated brain tissue segmentation into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple sclerosis, etc.
no code implementations • 25 Nov 2017 • Anand Gupta, Hardeo Thakur, Ritvik Shrivastava, Pulkit Kumar, Sreyashi Nag
In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), using the popular concept of Cascade Learning.
2 code implementations • 23 Nov 2017 • Pulkit Kumar, Monika Grewal, Muktabh Mayank Srivastava
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases.
no code implementations • 25 Oct 2017 • Srikrishna Varadarajan, Muktabh Mayank Srivastava, Monika Grewal, Pulkit Kumar
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans.
no code implementations • 13 Oct 2017 • Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar, Srikrishna Varadarajan
Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level.