405 citations · 595 across the 12 of their papers we have counts for
12 papers
Multimodal Integration Challenges in Emotionally Expressive Child Avatars for Training Applications
Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita +2
Dynamic facial emotion is essential for believable AI-generated avatars, yet most systems remain visually static, limiting their use in simulations like virtual training for invest…
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…
VISEM-Tracking, a human spermatozoa tracking dataset
Vajira Thambawita, Steven A. Hicks, Andrea M. Storås +7
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manu…
Combining datasets to increase the number of samples and improve model fitting
Thu Nguyen, Rabindra Khadka, Nhan Phan +3
For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from a…
Video Analytics in Elite Soccer: A Distributed Computing Perspective
Debesh Jha, Ashish Rauniyar, Håvard D. Johansen +4
Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training,…
Principal Component Analysis based frameworks for efficient missing data imputation algorithms
Thu Nguyen, Hoang Thien Ly, Michael Alexander Riegler +2
Missing data is a commonly occurring problem in practice. Many imputation methods have been developed to fill in the missing entries. However, not all of them can scale to high-dim…
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…
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar +4
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the…
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal +6
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial…
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…
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler +5
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Imag…
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar +5
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and…