Tootfinder

MitoDetect : A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping
Esha Sadia Nasir, Jiaqi Lv, Mostafa Jahanifer, Shan E Ahmed Raza
https://arxiv.org/abs/2509.02586

MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping
Automated detection and classification of mitotic figures especially distinguishing atypical from normal remain critical challenges in computational pathology. We present MitoDetect++, a unified deep learning pipeline designed for the MIDOG 2025 challenge, addressing both mitosis detection and atypical mitosis classification. For detection (Track 1), we employ a U-Net-based encoder-decoder architecture with EfficientNetV2-L as the backbone, enhanced with attention modules, and trained via combi…

Compositional Interface Refinement Through Subtyping in Probabilistic Session Types
Paula Blechschmidt
https://arxiv.org/abs/2509.16228 https://arxiv.org/p…

Compositional Interface Refinement Through Subtyping in Probabilistic Session Types
Multiparty session types (MPST) are a robust typing framework that ensures safe and deadlock-free communication within distributed protocols. As these protocols grow in complexity, compositional modelling becomes increasingly important to scalably verify their behaviour. Therefore, we propose using a refinement-based subtyping approach to facilitate the modularity needed for compositional verification. Subtyping in classic MPST systems inherently represents a notion of refinement: A larger type…

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Dexuan He, Xiao Zhou, Wenbin Guan, Liyuan Zhang, Xiaoman Zhang, Sinuo Xu, Ge Wang, Lifeng Wang, Xiaojun Yuan, Xin Sun, Yanfeng Wang, Kun Sun, Ya Zhang, Weidi Xie
https://arxiv.org/abs/2508.15904

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over 70% of cases. While pathology vision-language (VL) foundation models show promising zero-shot capabilities for common cancer subtyping, their clinical performance for rare cancers remains limited. Existing multi-instance learning (MIL) methods rely only on visual features, overlooking cross-modal knowledge and compro…

interesting discussion about algebraic types … all the examples of type subtraction are in a context that has subtyping
https://lobste.rs/s/jxprbr/algebraic_types_are_not_scary_actually#c_271guc