27 citations · 32 across the 12 of their papers we have counts for
12 papers
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Abhishek Srivastava, Debesh Jha, Bulent Aydogan +2
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal…
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Abhishek Srivastava, Debesh Jha, Elif Keles +3
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonst…
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network
Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci +1
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorecta…
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci +1
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve…
Challenges and Solutions to Build a Data Pipeline to Identify Anomalies in Enterprise System Performance
Xiaobo Huang, Amitabha Banerjee, Chien-Chia Chen +4
We discuss how VMware is solving the following challenges to harness data to operate our ML-based anomaly detection system to detect performance issues in our Software Defined Data…
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
Abhishek Srivastava, Sukalpa Chanda, Debesh Jha +4
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing t…
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
Abhishek Srivastava, Sukalpa Chanda, Debesh Jha +2
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to…
Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification
Abhishek Srivastava, Sukalpa Chanda, Umapada Pal
Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier write…
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination
Abhishek Srivastava, Sukalpa Chanda, Umapada Pal
The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural netwo…
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data
Seemandhar Jain, Prarthi Jain, Abhishek Srivastava
Anomaly detection is critical in various fields, including intrusion detection, health monitoring, fault diagnosis, and sensor network event detection. The isolation forest (or iFo…
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Abhishek Srivastava, Debesh Jha, Sukalpa Chanda +6
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of…
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Nandan Thakur, Nils Reimers, Andreas Rücklé +2
Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution…