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Found 15 papers, 5 papers with code
Overcoming Scene Context Constraints for Object Detection in wild using Defilters
Our experiments demonstrate that utilizing defiltered images significantly improves mean average precision compared to training object detection models on distorted images.
Towards understanding the nature of direct functional connectivity in visual brain network
In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86. 5% to 91. 5% for positively correlated VBN, which is 2% greater than that using negative correlation.
Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI
The proposed approach is employed for classification of a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI), sourced from ADNI dataset.
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment
After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI.
MCI Detection using fMRI time series embeddings of Recurrence plots
Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof.
Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning
To achieve this, 0- and 1-dimensional persistence diagrams are computed for each visual network representing COCO, ImageNet, and SUN.
Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision
The obtained result of this analysis has established a baseline in studying how differently human brain functions while looking into images of diverse complexities.
Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision
Subsequently, parallel CNN model is employed that uses combined 2D features for classifying images across COCO, Imagenet and SUN.
Identification of Stochasticity by Matrix-decomposition: Applied on Black Hole Data
Proposed algorithm is applied on astronomical data: 12 temporal-classes of timeseries of black hole GRS 1915+105, obtained from RXTE satellite with average length 25000.
Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5
Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades.
Identifying Stochasticity in Time-Series with Autoencoder-Based Content-aware 2D Representation: Application to Black Hole Data
An autoencoder is trained with a loss function to learn latent space (using both time- and frequency domains) representation, that is designed to be, time-invariant.
SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation
To address this, we propose a novel evaluation metric for segmentation performance, emphasizing smaller segments, by assigning higher weightage to smaller segment pixels.
Using Topological Framework for the Design of Activation Function and Model Pruning in Deep Neural Networks
Two independent contributions of this paper are 1) Novel activation function for faster training convergence 2) Systematic pruning of filters of models trained irrespective of activation function.
Towards Learning a Vocabulary of Visual Concepts and Operators using Deep Neural Networks
Deep neural networks have become the default choice for many applications like image and video recognition, segmentation and other image and video related tasks. However, a critical challenge with these models is the lack of explainability. This requirement of generating explainable predictions has motivated the research community to perform various analysis on trained models. In this study, we analyze the learned feature maps of trained models using MNIST images for achieving more explainable predictions. Our study is focused on deriving a set of primitive elements, here called visual concepts, that can be used to generate any arbitrary sample from the data generating distribution. We derive the primitive elements from the feature maps learned by the model. We illustrate the idea by generating visual concepts from a Variational Autoencoder trained using MNIST images. We augment the training data of MNIST dataset by adding about 60, 000 new images generated with visual concepts chosen at random. With this we were able to reduce the reconstruction loss (mean square error) from an initial value of 120 without augmentation to 60 with augmentation. Our approach is a first step towards the final goal of achieving trained deep neural network models whose predictions, features in hidden layers and the learned filters can be well explained. Such a model when deployed in production can easily be modified to adapt to new data, whereas existing deep learning models need a re training or fine tuning.
Cognitive state classification using transformed fMRI data
The novelty of the proposed method lies in utilizing the phase information in the transformed domain, for classifying between the cognitive tasks along with random sieve function chosen with a particular probability distribution.
