Vision Transformers

CellViT++: The AI That Learned to Read Cells Without a Pathologist’s Handbook.

CellViT++: The AI That Learned to Read Cells Without a Pathologist’s Handbook

Leave a Comment / Machine Learning, Computer Vision, Medical AI /

CellViT++: The AI That Learned to Read Cells Without a Pathologist’s Handbook | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About Digital Pathology AI · arXiv:2501.05269 · January 2025 · 22 min read CellViT++: The AI That Learned to Read Cells Without a Pathologist’s Handbook Researchers at University Hospital Essen built a […]

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The WEMoE framework transforms critical MLP modules into dynamic mixture-of-experts structures while statically merging non-critical components. Input-dependent routing weights allow the model to adaptively blend task-specific knowledge, achieving superior multi-task performance over static merging methods.

WEMoE: How a Mixture-of-Experts Approach Is Solving the Multi-Task Model Merging Problem

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WEMoE: How a Mixture-of-Experts Approach Is Solving the Multi-Task Model Merging Problem | MedAI Research MedAI Research Machine Learning About Deep Learning · TPAMI, 2026 · 18 min read The Static Model Merging Problem — and How WEMoE Learned to Adapt WEMoE introduces a dynamic mixture-of-experts approach to multi-task model merging, transforming how we combine

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Diagram illustrating the FRIES framework for estimating inconsistency in saliency metrics across deep learning models and perturbations.

FRIES: A Groundbreaking Framework for Inconsistency Estimation of Saliency Metrics

1 Comment / Machine Learning /

Unlocking Trust in AI: Introducing FRIES – The First Framework for Inconsistency Estimation of Saliency Metrics As artificial intelligence (AI) becomes increasingly embedded in high-stakes domains like healthcare, finance, and autonomous systems, the need for explainable AI (XAI) has never been greater. One of the most widely used tools in XAI is the saliency map,

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A deep vision-transformer metadata-fusing framework is developed for skin lesion classification. In this framework, a novel Deep Metadata Fusion Module (DMFM) is proposed to embed encoded metadata onto the image embedding during the compression, mimicking the process of "forging" decoration onto a metal piece, hence the name DeepMetaForge

DeepMetaForge: Revolutionizing Skin Lesion Classification with Vision-Transformers and Metadata Fusion (2023 Study)

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Introduction: The Urgent Need for Accurate Skin Cancer Detection Skin cancer is one of the most prevalent and potentially deadly diseases globally, with early detection being crucial for successful treatment. According to the American Cancer Society, melanoma alone accounts for over 7,000 deaths annually in the United States. Traditional diagnostic methods rely heavily on visual

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