Anagha Harindranath: TranSegNet Highlights The Growing Importance of AI in Advancing Real-World Clinical Applications
Anagha Harindranath, Master of Technology in Artificial Intelligence at Amrita Vishwa Vidyapeetham, reposted from Amrita School of Computing, Amritapuri on LinkedIn:
”I’m excited to share that our research paper titled ‘TranSegNet: A Transformer Guided Framework for Enhanced Segmentation of Infarct Core and Affected ASPECTS Regions for Ischemic Stroke Assessment’ has been published in the proceedings of WACV (Winter Conference on Applications of Computer Vision) 2026 organized by the Computer Vision Foundation (CVF).
The work was presented as part of the workshop ‘Pixels to Patients: Bridging CV State-of-the-Art with Clinical Impact’, highlighting the growing role of AI in advancing real-world clinical applications.
This work focuses on improving the accuracy and consistency of segmenting infarct core and affected ASPECTS regions from Non-Contrast CT (NCCT) scans, contributing towards more reliable and efficient stroke assessment.
Through this research, we developed a transformer-guided deep learning framework ‘TranSegNet’, that enhances segmentation performance in medical images.
I sincerely thank Dr. Vivek Menon and Sony M S for their constant guidance and support throughout this work.
I’m also grateful to my co-authors Dr. Sumi Suresh and Dr.Vivek Nambiar for their valuable collaboration and contributions.
Looking forward to exploring more advancements in AI for healthcare and medical imaging.”
Amrita School of Computing, Amritapuri shared a post on LinkedIn about a recent article by Anagha Harindranath et al, adding;
”We are proud to announce that a research paper titled ‘TranSegNet: A Transformer Guided Framework for Enhanced Segmentation of Infarct Core and Affected ASPECTS Regions for Ischemic Stroke Assessment’ has been published in the proceedings of WACV 2026 (Winter Conference on Applications of Computer Vision), a prestigious CORE ‘A’ ranked conference organized by the Computer Vision Foundation (CVF).
The paper was presented as part of the workshop ‘Pixels to Patients: Bridging CV State-of-Art with Clinical Impact’, underscoring the transformative role of AI in advancing clinical applications.
This significant achievement is the result of a collaborative effort by Anagha Harindranath (MTech AI 2024–26, Amrita School of Computing), Sony M S (PhD Scholar, Amrita School of Computing), Sumi Suresh (University at Buffalo, New York, USA), Dr.Vivek Nambiar (Amrita Hospital, Kochi), and Dr. Vivek Menon (Professor and Research Head, Amrita School of Computing, Amritapuri).
The research focuses on leveraging transformer-based deep learning techniques to improve the accuracy and reliability of ischemic stroke assessment, contributing meaningfully to the intersection of AI and healthcare.
The paper was presented on March 6, 2026, in Tucson, Arizona, USA.
Congratulations to the entire team on this remarkable accomplishment.”
Title: TranSegNet: A Transformer Guided Framework for Enhanced Segmentation of Infarct Core and Affected ASPECTS Regions for Ischemic Stroke Assessment
Authors: Anagha Harindranath, Sony M S, Sumi Suresh M S, Vivek Nambiar, Vivek Menon
Read the Full Article for for IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops
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