32 citations · 117 across the 13 of their papers we have counts for
13 papers
Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Yumi Lee, Harim Oh, Hyoryung Kim +52
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remai…
Exploring Weakly Supervised Semantic Segmentation Ensembles for Medical Imaging Systems
Erik Ostrowski, Bharath Srinivas Prabakaran, Muhammad Shafique
Reliable classification and detection of certain medical conditions, in images, with state-of-the-art semantic segmentation networks, require vast amounts of pixel-wise annotation.…
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks
Mahum Naseer, Bharath Srinivas Prabakaran, Osman Hasan +1
Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. H…
SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters
Erik Ostrowski, Bharath Srinivas Prabakaran, Muhammad Shafique
Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability. Although state-of-the-art networks deliver r…
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images
Bharath Srinivas Prabakaran, Erik Ostrowski, Muhammad Shafique
Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for gener…
FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features
Bharath Srinivas Prabakaran, Paul Hamelmann, Erik Ostrowski +1
Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation. However, the interpretation of…
Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research…
ATLAS: An IoT Architecture and Secure Open-source Networking Stack for Anonymous Localization and Tracking Using Smartphones and Bluetooth Beacons
Bharath Srinivas Prabakaran, Felix Fasching, Juri Schreib +2
Bluetooth (BT) has revolutionized close-range communication enabling smart capabilities in everyday devices through wireless technology. One of the most important sub-domains of In…
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables
Bharath Srinivas Prabakaran, Asima Akhtar, Semeen Rehman +2
In this work, we propose the BioNetExplorer framework to systematically generate and explore multiple DNN architectures for bio-signal processing in wearables. Our framework adapts…
ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
There has been abundant research on the development of Approximate Circuits (ACs) for ASICs. However, previous studies have illustrated that ASIC-based ACs offer asymmetrical gains…
EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction
Bharath Srinivas Prabakaran, Alberto García Jiménez, Germán Moltó Martínez +1
State-of-the-art techniques for detecting, or predicting, neurological disorders (1) focus on predicting each disorder individually, and are (2) computationally expensive, leading…
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bharath Srinivas Prabakaran, Semeen Rehman, Muhammad Shafique
Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-…
Architectural-Space Exploration of Heterogeneous Reliability and Checkpointing Modes for Out-of-Order Superscalar Processors
Bharath Srinivas Prabakaran, Mihika Dave, Florian Kriebel +2
Reliability has emerged as a key topic of interest for researchers around the world to detect and/or mitigate the side effects of decreasing transistor sizes, such as soft errors.…