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arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,...
arXiv:2607. 14328v1 Announce Type: cross Abstract: In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast.
The paper proposes a scalable framework that uses cell-level spatial transcriptomics (ST) as weak supervision for nuclei classification in histopathology images. By converting gene expression profiles into cell-type labels and leveraging nucleus masks from ST data, the method automatically generates image-label pairs without manual annotation. Evaluations on unseen tissues show that this approach achieves segmentation and classification performance comparable to conventional supervised methods while requiring fewer manually annotated datasets.
arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.
CLARiTy is a vision transformer model that jointly performs multi‑label classification and weakly‑supervised localization of chest X‑ray pathologies. It uses class‑specific tokens to produce attention maps and a SegmentCAM module to refine foreground segmentation with anatomical priors. Trained on NIH ChestXray14 image‑level labels and distilled from a ConvNeXtV2 teacher, CLARiTy achieves competitive classification and strong localization, especially for small pathologies, outperforming prior methods by up to 50.7% relative Macro IoU accuracy.