Search Results for author:
Found 8 papers, 1 papers with code
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
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.
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
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.
LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer
Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers.
U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool
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.
A Big Data Analysis Framework Using Apache Spark and Deep Learning
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.
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases.
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
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.
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
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.
