Dr. Binod Bhattarai

Dr. Binod Bhattarai

  • Board Member and Adjunct Research Scientist at BBMMLL research group, NAAMII

  • Assistant Professor at University of Aberdeen

  • Post-Doctoral Researcher at Imperial College London (Computer Vision and Learning Lab)

  • Specializes in reinforcement learning and adversarial learning for face analysis

Dr. Binod Bhattarai is an Assistant Professor at University of Aberdeen and previously a Post-Doctoral Researcher from Imperial College London's Computer Vision and Learning Lab, where he focuses on reinforcement learning and adversarial learning techniques for automatic face analysis. He earned his PhD from Université de Caen in 2016, where his research centered on distance metric learning and deep learning approaches for facial recognition and analysis. His work bridges theoretical advances in machine learning with practical applications in biometric systems and computer vision. At NAAMII, Dr. Bhattarai leads the B Bhattarai Multimodal Learning Lab as an adjunct research scientist, advancing AI research with applications relevant to developing regions.

Publications

2025
Out of Distribution Detection in Gastrointestinal Vision by Estimating Nearest Centroid Distance Deficit
Sandesh Pokhrel, Sanjay Bhandari, Sharib Ali, Tryphon Lambrou, Anh Nguyen, Yash Raj Shrestha, Angus Watson, Danail Stoyanov, Prashnna Gyawali, Binod Bhattarai
2025
Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models
Bidur Khanal, Sandesh Pokhrel, Sanjay Bhandari, Ramesh Rana, Nikesh Shrestha, Ram Bahadur Gurung, Cristian Linte, Angus Watson, Yash Raj Shrestha, Binod Bhattarai
2025
Multimodal Federated Learning With Missing Modalities through Feature Imputation Network
Pranav Poudel, Aavash Chhetri, Prashnna Gyawali, Georgios Leontidis, Binod Bhattarai
2025
NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance
Anju Chhetri, Jari Korhonen, Prashnna Gyawali, Binod Bhattarai
2025
Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation
2024
T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification
Sudarshan Regmi, Bibek Panthi, Yifei Ming, Prashnna Gyawali, Danail Stoyanov, Binod Bhattarai
2024
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical Heterogeneity
Sanskar Amgain, Prashant Shrestha, Sophia Bano, Ignacio del Valle Torres, Michael Cunniffe, Victor Hernandez, Phil Beales, Binod Bhattarai
2024
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Bidur Khanal, Tianhong Dai, Binod Bhattarai, Cristian Linte
2024
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities
Pranav Poudel, Prashant Shrestha, Sanskar Amgain, Yash Raj Shrestha, Prashnna Gyawali, Binod Bhattarai
2024
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Tryphon Lambrou, Prashnna Gyawali, Binod Bhattarai
2024
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Yash Raj Shrestha, Binod Bhattarai
2024
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
2024
ReweightOOD: Loss Reweighting for Distance-based OOD Detection
Sudarshan Regmi, Bibek Panthi, Yifei Ming, Prashnna K Gyawali, Danail Stoyanov, Binod Bhattarai
2024
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
2023
Metric Transform: Exploring beyond Affine Transform for Neural Networks
2023
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
2023
CholecTriplet2022: Show me a tool and tell me the triplet–an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye, Pranav Poudel, Binod Bhattarai, Shrawan Kumar Thapa, Nicolas Padoy
2023
Neural Network Pruning for Real-time Polyp Segmentation
2023
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Danail Stoyanov, Cristian A Linte
2023
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection
Sudarshan Regmi, Bibek Panthi, Sakar Dotel, Prashnna K Gyawali, Danail Stoynov, Binod Bhattarai
2023
Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation
Binod Bhattarai, Ronast Subedi, Rebati Raman Gaire, Eduard Vazquez, Danail Stoyanov
2023
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Abdul Qayyum, Abdesslam Benzinou, Moona Mazher, Fabrice Meriaudeau, Chiara Lena, Ilaria Anita Cintorrino, Gaia Romana De Paolis, Jessica Biagioli, Daria Grechishnikova, Jing Jiao, Bizhe Bai, Yanyan Qiao, Binod Bhattarai, Rebati Raman Gaire, Ronast Subedi, Eduard Vazquez, Szymon Płotka, Aneta Lisowska, Arkadiusz Sitek, George Attilakos, Ruwan Wimalasundera, Anna L David, Dario Paladini, Jan Deprest, Elena De Momi, Leonardo S Mattos, Sara Moccia, Danail Stoyanov
2023
Input Invex Neural Network
2023
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Ronast Subedi, Rebati Raman Gaire, Sharib Ali, Anh Nguyen, Danail Stoyanov, Binod Bhattarai
2022
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
2022
NepBERTa: Nepali Language Model Trained in a Large Corpus
2022
Task-Aware Active Learning for Endoscopic Polyp Segmentation
2022
Task-Aware Active Learning for Endoscopic Image Analysis
2022
Label Geometry Aware Discriminator for Conditional Generative Networks