Dicken SC Ko, Amy Lorincz, Sukesh Adiga, Timothée Bernard, Rachel Cohen, Maciej Lacki, M Carmen Mir - Real-Time Smoke Reduction in Minimally Invasive Surgery: Evaluating CLARIS for Artificial Intelligence-Driven Visualization in Robotic-Assisted Laparoscopic Prostatectomy, The Journal of Urology, 2025 (Impact Factor: 7.5)
Maciej Łącki, Megha Kalia, Nidhi Abraham, Sukesh Adiga Vasudeva, Dicken SC Ko, Timothée Bernard, Amy Lorincz - Quantifying Lens Obstructions in Minimally Invasive Surgery: The Impact on Performance and Outcomes, Frontiers in Surgery, 2025 (Impact Factor: 1.9)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation, Machine Learning for Biomedical Imaging (MELBA), 2025.
Balamurali Murugesan, Sukesh Adiga V, Bingyuan Liu, Herve Lombaert, Ismail Ben Ayed, Jose Dolz - Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints, Medical Image Analysis (MedIA), 2025 (Impact Factor: 10.7)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - Anatomically-aware Uncertainty for Semi-supervised Image Segmentation, Medical Image Analysis (MedIA), 2023 (Impact Factor: 10.7)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - Attention-based Dynamic Subspace Learners for Medical Image Analysis, IEEE Journal of Biomedical And Health Informatics (JBHI), 2022 (Impact Factor: 7.7)
Balamurali Murugesan, Sukesh Adiga V, Bingyuan Liu, Herve Lombaert, Ismail Ben Ayed, Jose Dolz - Trust your neighbours: Penalty-based constraints for model calibration, in proc. of 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023 (Acceptance < 32%)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - GeoLS: Geodesic Label Smoothing for Image Segmentation, in 6th edition of Medical Imaging with Deep Learning (MIDL), 2023 (Acceptance < 42%, Oral Presentation ~ Top 12%)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation, in proc. of 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022 (Acceptance < 31%)
Sukesh Adiga V, Jayanthi Sivaswamy - Matching the characteristics of Fundus and Smartphone camera images, in Proc. of 16th IEEE International Symposium on Biomedical Imaging (ISBI), 2019 (Acceptance < 35%)
Sukesh Adiga V, Jayanthi Sivaswamy - Shared Encoder based Denoising of Optical Coherence Tomography Images, in Proc. of 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2018 (Acceptance < 33%)
Sukesh Adiga V, and Jayanthi Sivaswamy - FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks, Inpainting and Denoising Challenges, ECCV workshop, Springer, 2019 (Oral Presentation, Ranked 3rd)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - Attention-based Dynamic Subspace Learners, in 5th Medical Imaging with Deep Learning (MIDL), 2022. (Short paper)
Sukesh Adiga V, Jose Dolz, Herve Lombaert - Manifold-driven Attention Maps for Weakly Supervised Segmentation, Arxiv, 2020
Laurent Chauvin, Sukesh Adiga V, Jose Dolz, Herve Lombaert, Matthew Toews - A Large-scale Neuroimage Analysis using Keypoint Signatures: UK Biobank (Abstract), Organization for Human Brain Mapping (OHBM), 2020
Sukesh Adiga V, Learning with Uncertainty in Medical Image Segmentation, PhD thesis (Gold Medal & Excellence), July 2024.
Sukesh Adiga V, Retinal Image Quality Improvement via Learning, Master's thesis, August 2019.