1 code implementation • 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.
2 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.