Iyyakutti Iyappan Ganapathi — accepted AI-conference papers

2025

DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation

Maregu Assefa, Muzammal Naseer, Iyyakutti Iyappan Ganapathi, Syed Sadaf Ali, Mohamed L Seghier, Naoufel Werghi

CVPR 2025poster

Semi-supervised learning in medical image segmentation leverages unlabeled data to reduce annotation burdens through consistency learning. However, current methods struggle with class imbalance and high uncertainty from pathology variations, leading to inaccurate segmentation in 3D medical images. T…

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2025

Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation

Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi, Basit Alawode, Asim Khan, Sajid Javed +3

CVPR 2025poster

In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a single magnification level. However, this approach might not be sufficient for tasks like cancer subtype classification, tis…

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2024

CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment

Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo, Naoufel Werghi, Mohammed Bennamoun

CVPR 2024poster

This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP) a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision language models by leveraging extens…

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