{"ID":2895650,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08460","arxiv_id":"2507.08460","title":"F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement","abstract":"F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multimodal inputs, limited generalizability, and narrow task specificity. Through flexible synthetic modality training, F3-Net maintains robust performance even in the presence of missing MRI sequences, leveraging a zero-image strategy to substitute absent modalities without relying on explicit synthesis networks, thereby enhancing real-world applicability. Its unified architecture supports multi-pathology segmentation across glioma, metastasis, stroke, and white matter lesions without retraining, outperforming CNN-based and transformer-based models that typically require disease-specific fine-tuning. Evaluated on diverse datasets such as BraTS 2021, BraTS 2024, and ISLES 2022, F3-Net demonstrates strong resilience to domain shifts and clinical heterogeneity. On the whole pathology dataset, F3-Net achieves average Dice Similarity Coefficients (DSCs) of 0.94 for BraTS-GLI 2024, 0.82 for BraTS-MET 2024, 0.94 for BraTS 2021, and 0.79 for ISLES 2022. This positions it as a versatile, scalable solution bridging the gap between deep learning research and practical clinical deployment.","short_abstract":"F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multimodal inputs, limited generalizability, and narrow task specificity. Through flexible synthetic modality training, F3-Net maintains robust performance even in the presence...","url_abs":"https://arxiv.org/abs/2507.08460","url_pdf":"https://arxiv.org/pdf/2507.08460v1","authors":"[\"Seyedeh Sahar Taheri Otaghsara\",\"Reza Rahmanzadeh\"]","published":"2025-07-11T10:03:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}