{"ID":2832033,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06849","arxiv_id":"2512.06849","title":"Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT","abstract":"Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often resemble benign degenerative changes. We introduce a 2D weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks. The method combines a Diffusion Autoencoder (DAE) that produces a classifier-guided healthy edit of each vertebra with pixel-wise difference maps that propose suspect candidate lesions. To determine which regions truly reflect malignancy, we introduce Hide-and-Seek Attribution: each candidate is revealed in turn while all others are hidden, the edited image is projected back to the data manifold by the DAE, and a latent-space classifier quantifies the isolated malignant contribution of that component. High-scoring regions form the final lytic or blastic segmentation. On held-out radiologist annotations, we achieve strong blastic/lytic performance despite no mask supervision (F1: 0.91/0.85; Dice: 0.87/0.78), exceeding baselines (F1: 0.79/0.67; Dice: 0.74/0.55). These results show that vertebra-level labels can be transformed into reliable lesion masks, demonstrating that generative editing combined with selective occlusion supports accurate weakly supervised segmentation in CT.","short_abstract":"Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often resemble benign degenerative changes. We introduce a 2D weakly supervised method trained solely on vertebra-level healthy/malignant labels, w...","url_abs":"https://arxiv.org/abs/2512.06849","url_pdf":"https://arxiv.org/pdf/2512.06849v2","authors":"[\"Matan Atad\",\"Alexander W. Marka\",\"Lisa Steinhelfer\",\"Anna Curto-Vilalta\",\"Yannik Leonhardt\",\"Sarah C. Foreman\",\"Anna-Sophia Walburga Dietrich\",\"Robert Graf\",\"Alexandra S. Gersing\",\"Bjoern Menze\",\"Daniel Rueckert\",\"Jan S. Kirschke\",\"Hendrik Möller\"]","published":"2025-12-07T14:03:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}