bibtext: @InProceedings{ Don_Multistage_MICCAI2024,
                   author = { Dong, Haichuan and Zhou, Runjie and Yun, Boxiang and Zhou, Huihui and Zhang, Benyan and Li, Qingli and Wang, Yan },
                   title = { { Multi-stage Multi-granularity Focus-tuned Learning Paradigm for Medical HSI Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Despite significant breakthrough in computational pathology that Medical Hyperspectral Imaging (MHSI) has brought, the asymmetric information in spectral and spatial dimensions pose a primary challenge. In this study, we propose a multi-stage multi-granularity Focus-tuned Learning paradigm for Medical HSI Segmentation. To learn subtle spectral differences while equalizing the spatiospectral feature learning, we design a quadruplet learning pre-training and focus-tuned fine-tuning stages for capturing both disease-level and image-level subtle spectral differences while integrating spatially and spectrally dominant features. We propose an intensifying and weakening strategy throughout all stages. Our method significantly outperforms all competitors in MHSI segmentation, with over 3.5% improvement in DSC. Ablation study further shows our method learns compact spatiospectral features while capturing various levels of spectral differences. Code will be released at https://github.com/DHC233/FL.
title: Multi-stage Multi-granularity Focus-tuned Learning Paradigm for Medical HSI Segmentation
authors: Dong, Haichuan Zhou, Runjie Yun, Boxiang Zhou, Huihui Zhang, Benyan Li, Qingli Wang, Yan