Cagla also has an accepted paper here at WACV. Don’t miss her Oral and Poster presentations today: LLM-generated Rewrite and Context Modulation for Enhanced Vision Language Models in Digital Pathology. Her paper introduces a novel approach to improving vision-language models (VLMs) for digital pathology, addressing key challenges such as limited largescale datasets and the sensitivity of zero-shot classification tasks to prompt variations. To overcome these limitations, the study leverages large language models (LLMs) to generate enriched language rewrites for a public pathology dataset, demonstrating that this augmentation enhances performance in tasks like zero-shot classification and text-to-image and image-to-text retrieval. Additionally, the paper presents a context modulation layer that refines image embeddings to better align with paired text, further improving model accuracy. As part of this work, the study constructs the largest publicly available pathology caption dataset to date, comprising 8 million captions. These advancements demonstrate the value of carefully leveraged synthetic data in building more robust and reliable multimodal models for medical imaging. 2.3.4 From Visual Explanations to Counterfactual Explanations with Latent Diffusion 3.2.5 Uncertainty-based Data-wise Label Smoothing for Calibrating Multiple Instance Learning in Histopathology Image Classification 2.13 MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training 2.27 CusConcept: Customized Visual Concept Decomposition with Diffusion Models Cagla’s picks of the day: Cagla Deniz Bahadir is a finalyear PhD candidate in Biomedical Engineering at Cornell University. Her research focuses on the intersection of Machine Learning and Medical Imaging, with an emphasis on enhancing the reliability and robustness of medical visionlanguage models. For today, Saturday 1 2 Cagla’s Picks DAILY WACV Saturday Orals Posters
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