1 citations · 1 across the 7 of their papers we have counts for
9 papers
DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets
Vishal, Arnav Aditya, Nitin Kumar +1
Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to common conditions. This imbalanc…
ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin Kumar
In biomedical and neurodegenerative disorders, accurate and early disease identification remains challenging due to the scarcity of labeled data and the complexity of imaging patte…
ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification
VSS Tejaswi Abburi, Ananya Singhal, Saurabh J. Shigwan +1
Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of progression to severe disease…
UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets
Arnav Aditya, Nitin Kumar, Saurabh Shigwan
Driven by advancements in deep learning, computer-aided diagnoses have made remarkable progress. However, outside controlled laboratory settings, algorithms may encounter several c…
Does the Skeleton-Recall Loss Really Work?
Devansh Arora, Nitin Kumar, Sukrit Gupta
Image segmentation is an important and widely performed task in computer vision. Accomplishing effective image segmentation in diverse settings often requires custom model architec…
UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering
A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar
The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segment…
UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters
Kovvuri Sai Gopal Reddy, Bodduluri Saran, A. Mudit Adityaja +3
The data-hungry approach of supervised classification drives the interest of the researchers toward unsupervised approaches, especially for problems such as medical image segmentat…
SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image Segmentation
Chanda Grover Kamra, Indra Deep Mastan, Nitin Kumar +1
Recent developments in self-supervised learning (SSL) have made it possible to learn data representations without the need for annotations. Inspired by the non-contrastive SSL appr…
UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks
Kovvuri Sai Gopal Reddy, Bodduluri Saran, A. Mudit Adityaja +2
Image segmentation, the process of partitioning an image into meaningful regions, plays a pivotal role in computer vision and medical imaging applications. Unsupervised segmentatio…