Tootfinder

Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Yingyu Yang, Qianye Yang, Kangning Cui, Can Peng, Elena D'Alberti, Netzahualcoyotl Hernandez-Cruz, Olga Patey, Aris T. Papageorghiou, J. Alison Noble
https://arxiv.org/abs/2507.05154…

Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detection, typically require extensive annotations, which is time-consuming and labor-intensive. In this paper, we present an unsupervised framework for end-diastole (ED) and end-systole (ES) detection through self-supervised learning of latent cardiac motion trajectories from 4-chamber-view echocardiography videos. Our meth…

Local Type Inference for Context-Free Session Types
Bernardo Almeida (LASIGE, Faculty of Sciences, University of Lisbon, Portugal), Andreia Mordido (LASIGE, Faculty of Sciences, University of Lisbon, Portugal), Vasco T. Vasconcelos (LASIGE, Faculty of Sciences, University of Lisbon, Portugal)
https://arxiv.org/abs/2505.20855

Local Type Inference for Context-Free Session Types
We address the problem of local type inference for a language based on System F with context-free session types. We present an algorithm that leverages the bidirectional type checking approach to propagate type information, enabling first class polymorphism while addressing the intricacies brought about by the sequential composition operator and type equivalence. The algorithm improves the language's usability by eliminating the need for type annotations at type application sites.

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz, Abhinav Kumar, Sung Eun Kim, Xiaoman Zhang, Miao Zhu, Mahmoud Hussain Alabbad, Maha Sbayel Alhazmi, Neel P. Mistry, Kent Ryan Kleinschmidt, Brady Chrisler, Sathvik Suryadevara, Sri Sai Dinesh Jaliparthi, Noah Michael Prudlo, Mark David Marino, Jeremy Palacio, Rithvik Akula, Hong-Yu Zhou, Ibrahim Ethem Hamamci, Scott J. Adams, Hassan Rayhan AlOmaish, P…

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
We present ReXGroundingCT, the first publicly available dataset to link free-text radiology findings with pixel-level segmentations in 3D chest CT scans that is manually annotated. While prior datasets have relied on structured labels or predefined categories, ReXGroundingCT captures the full expressiveness of clinical language represented in free text and grounds it to spatially localized 3D segmentation annotations in volumetric imaging. This addresses a critical gap in medical AI: the abilit…