no code implementations • 8 Oct 2024 • Corban Rivera, Grayson Byrd, William Paul, Tyler Feldman, Meghan Booker, Emma Holmes, David Handelman, Bethany Kemp, Andrew Badger, Aurora Schmidt, Krishna Murthy Jatavallabhula, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath, Rama Chellappa
Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment.
no code implementations • 1 Oct 2024 • Hongchao Shu, Mingxu Liu, Lalithkumar Seenivasan, Suxi Gu, Ping-Cheng Ku, Jonathan Knopf, Russell Taylor, Mathias Unberath
Extending 3D reconstruction to Augmented Reality (AR) applications, our solution offers AR assistance for articular notch measurement and annotation anchoring in a human-in-the-loop manner.
1 code implementation • 9 Aug 2024 • Long Bai, Guankun Wang, Mobarakol Islam, Lalithkumar Seenivasan, An Wang, Hongliang Ren
In particular, surgical VQA can enhance the interpretation of surgical data, aiding in accurate diagnoses, effective education, and clinical interventions.
1 code implementation • 16 Jul 2024 • Hao Ding, Tuxun Lu, Yuqian Zhang, Ruixing Liang, Hongchao Shu, Lalithkumar Seenivasan, Yonghao Long, Qi Dou, Cong Gao, Mathias Unberath
To address this limitation, we introduce the SegSTRONG-C challenge that aims to promote the development of algorithms robust to unforeseen but plausible image corruptions of surgery, like smoke, bleeding, and low brightness.
2 code implementations • 19 May 2023 • Long Bai, Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren
In this paper, we propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction.
1 code implementation • 19 Apr 2023 • Lalithkumar Seenivasan, Mobarakol Islam, Gokul Kannan, Hongliang Ren
Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image.
1 code implementation • 2 Feb 2023 • Mobarakol Islam, Lalithkumar Seenivasan, S. P. Sharan, V. K. Viekash, Bhavesh Gupta, Ben Glocker, Hongliang Ren
Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress.
1 code implementation • 28 Nov 2022 • Lalithkumar Seenivasan, Mobarakol Islam, Mengya Xu, Chwee Ming Lim, Hongliang Ren
Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.
3 code implementations • 22 Jun 2022 • Lalithkumar Seenivasan, Mobarakol Islam, Adithya K Krishna, Hongliang Ren
This overload often limits their time answering questionnaires from patients, medical students or junior residents related to surgical procedures.
6 code implementations • 10 Apr 2022 • Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu, Armine Vardazaryan, Fangfang Xia, Zixuan Zhao, Tong Xia, Fucang Jia, Yuxuan Yang, Hao Wang, Derong Yu, Guoyan Zheng, Xiaotian Duan, Neil Getty, Ricardo Sanchez-Matilla, Maria Robu, Li Zhang, Huabin Chen, Jiacheng Wang, Liansheng Wang, Bokai Zhang, Beerend Gerats, Sista Raviteja, Rachana Sathish, Rong Tao, Satoshi Kondo, Winnie Pang, Hongliang Ren, Julian Ronald Abbing, Mohammad Hasan Sarhan, Sebastian Bodenstedt, Nithya Bhasker, Bruno Oliveira, Helena R. Torres, Li Ling, Finn Gaida, Tobias Czempiel, João L. Vilaça, Pedro Morais, Jaime Fonseca, Ruby Mae Egging, Inge Nicole Wijma, Chen Qian, GuiBin Bian, Zhen Li, Velmurugan Balasubramanian, Debdoot Sheet, Imanol Luengo, Yuanbo Zhu, Shuai Ding, Jakob-Anton Aschenbrenner, Nicolas Elini van der Kar, Mengya Xu, Mobarakol Islam, Lalithkumar Seenivasan, Alexander Jenke, Danail Stoyanov, Didier Mutter, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Nicolas Padoy
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
2 code implementations • 28 Jan 2022 • Lalithkumar Seenivasan, Sai Mitheran, Mobarakol Islam, Hongliang Ren
Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding.
1 code implementation • 11 Sep 2021 • Mobarakol Islam, Lalithkumar Seenivasan, Hongliang Ren, Ben Glocker
In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence.
2 code implementations • 7 Jul 2020 • Mobarakol Islam, Lalithkumar Seenivasan, Lim Chwee Ming, Hongliang Ren
Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.