Tootfinder

Some cities are saying they won't indemnify officers in cases of wrongdoing. What does that mean for the victims of police misconduct?
https://www.themarshallproject.org/2025/11/01/police-accountability-liability-indemnify-civil-se…

How Some U.S. Cities Want to Avoid Paying for Police Misconduct
Denver, Minneapolis and other cities want to avoid paying large sums owed to victims of some types of misconduct. But will the officers pay up?

Do you agree with Friedrich Nietzsche’s critique of authority and herd mentality?
https://en.wikipedia.org/wiki/Friedrich_Nietzsche

🇺🇦 #NowPlaying on KEXP's #MiddayShow
The American Analog Set:
🎵 The Only One
#TheAmericanAnalogSet
https://open.spotify.com/track/2XItbN0HfeLpDamJzeQLqQ

Looks like the Peacemaker In Chief is gonna go blow up some Venezuela for Thanksgiving. 🇻🇪
😢
#war #TheAmericanFascist #venezuela
https://climatejustice.social/@PeterRu/115633789030686012

MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Yuheng Li, Yizhou Wu, Yuxiang Lai, Mingzhe Hu, Xiaofeng Yang
https://arxiv.org/abs/2509.02379 ht…

MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Accurate segmentation of organs and tumors in CT and MRI scans is essential for diagnosis, treatment planning, and disease monitoring. While deep learning has advanced automated segmentation, most models remain task-specific, lacking generalizability across modalities and institutions. Vision foundation models (FMs) pretrained on billion-scale natural images offer powerful and transferable representations. However, adapting them to medical imaging faces two key challenges: (1) the ViT backbone …

Touching the tumor boundary: A pilot study on ultrasound based virtual fixtures for breast-conserving surgery
Laura Connolly, Tamas Ungi, Adnan Munawar, Anton Deguet, Chris Yeung, Russell H. Taylor, Parvin Mousavi, Gabor Fichtinger Keyvan Hashtrudi-Zaad
https://arxiv.org/abs/2510.01452

Touching the tumor boundary: A pilot study on ultrasound based virtual fixtures for breast-conserving surgery
Purpose: Delineating tumor boundaries during breast-conserving surgery is challenging as tumors are often highly mobile, non-palpable, and have irregularly shaped borders. To address these challenges, we introduce a cooperative robotic guidance system that applies haptic feedback for tumor localization. In this pilot study, we aim to assess if and how this system can be successfully integrated into breast cancer care. Methods: A small haptic robot is retrofitted with an electrocautery blade t…

hey did you know: dragonfly nyphs have thrust vectoring in their assholes! #themoreyouknow

VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
Arman Behnam
https://arxiv.org/abs/2510.02086 https://arxiv.org/pdf/2510.020…

VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
Accurate detection and segmentation of brain tumors from magnetic resonance imaging (MRI) are essential for diagnosis, treatment planning, and clinical monitoring. While convolutional architectures such as U-Net have long been the backbone of medical image segmentation, their limited capacity to capture long-range dependencies constrains performance on complex tumor structures. Recent advances in diffusion models have demonstrated strong potential for generating high-fidelity medical images and…

🇺🇦 #NowPlaying on KEXP's #Roadhouse
The Marketts:
🎵 Out Of Limits
#TheMarketts
https://themarketts.bandcamp.com/track/out-of-limits
https://open.spotify.com/track/1RoyaDpjGZ718QrEgKtVY0

Mix-modal Federated Learning for MRI Image Segmentation
Guyue Hu, Siyuan Song, Jingpeng Sun, Zhe Jin, Chenglong Li, Jin Tang
https://arxiv.org/abs/2509.02541 https://

Mix-modal Federated Learning for MRI Image Segmentation
Magnetic resonance imaging (MRI) image segmentation is crucial in diagnosing and treating many diseases, such as brain tumors. Existing MRI image segmentation methods mainly fall into a centralized multimodal paradigm, which is inapplicable in engineering non-centralized mix-modal medical scenarios. In this situation, each distributed client (hospital) processes multiple mixed MRI modalities, and the modality set and image data for each client are diverse, suffering from extensive client-wise m…