Zobrazeno 1 - 10
of 534
pro vyhledávání: '"Raman Balasubramanian"'
Autor:
Handa, Palak, Mahbod, Amirreza, Schwarzhans, Florian, Woitek, Ramona, Goel, Nidhi, Dhir, Manas, Chhabra, Deepti, Jha, Shreshtha, Sharma, Pallavi, Thakur, Vijay, Gunjan, Deepak, Kakarla, Jagadeesh, Raman, Balasubramanian
We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine
Externí odkaz:
http://arxiv.org/abs/2408.04940
Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in diagnosing skin diseases, often outperforming dermatologists. However, they have also unveiled biases linked to specific demographic
Externí odkaz:
http://arxiv.org/abs/2405.10256
Autor:
Sen, Mrinmay, Qin, A. K., C, Gayathri, N, Raghu Kishore, Chen, Yen-Wei, Raman, Balasubramanian
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update in large-sc
Externí odkaz:
http://arxiv.org/abs/2403.02833
The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathetic, accurate, and engaging res
Externí odkaz:
http://arxiv.org/abs/2402.07640
In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents \texttt{DeepMediX}, a groundbreaking, resource-efficient model that
Externí odkaz:
http://arxiv.org/abs/2307.00324
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification
The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-pre
Externí odkaz:
http://arxiv.org/abs/2306.17794
In recent years, deep learning models have revolutionized medical image interpretation, offering substantial improvements in diagnostic accuracy. However, these models often struggle with challenging images where critical features are partially or fu
Externí odkaz:
http://arxiv.org/abs/2306.15574
Autor:
Nampalle, Kishore Babu, Singh, Pradeep, Uppala, Vivek Narayan, Gangwar, Sumit, Negi, Rajesh Singh, Raman, Balasubramanian
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking t
Externí odkaz:
http://arxiv.org/abs/2305.15426