Search Results for author: Prokar Dasgupta

Found 7 papers, 5 papers with code

DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography

1 code implementation19 Mar 2024 Zhenyu Bu, Yang Liu, Jiayu Huo, Jingjing Peng, Kaini Wang, Guangquan Zhou, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

Accurate identification of End-Diastolic (ED) and End-Systolic (ES) frames is key for cardiac function assessment through echocardiography.

Segmentation

SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative Planning

no code implementations10 Mar 2024 Maxence Boels, Yang Liu, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

In conclusion, SuPRA presents a new multi-task approach that paves the way for improved intra-operative assistance through surgical phase recognition and prediction of future events.

Surgical phase recognition

ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks

no code implementations27 Feb 2024 Yang Liu, Xiaomin Yu, Gongyu Zhang, Christos Bergeles, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

We train models for these tasks in a zero-shot cross-modal transfer setting, a domain where the previous state-of-the-art method relied on the fixed scale noise injection, often compromising the semantic content of the original modality embedding.

Domain Generalization Image Captioning +3

LoViT: Long Video Transformer for Surgical Phase Recognition

1 code implementation15 May 2023 Yang Liu, Maxence Boels, Luis C. Garcia-Peraza-Herrera, Tom Vercauteren, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos.

Online surgical phase recognition

SKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition

1 code implementation ICCV 2023 Yang Liu, Jiayu Huo, Jingjing Peng, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition.

Online surgical phase recognition

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