Tootfinder

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
https://arxiv.org/abs/2506.12363 …

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature e…

Fact Sheet: President Donald J. Trump Creates New Classification of Federal Employee to Help Serve the American People (The White House)
https://www.whitehouse.gov/fact-sheets/2025/07/fact-sheet-president-donald-j-trump-creates-new-classification-of-federal-employee-to-help-serve-the-american-people/
http://www.memeorandum.com/250717/p148#a250717p148

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
Long-Vu Hoang, Tuan Nguyen, Tran Huy Dat
https://arxiv.org/abs/2506.14148

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark compr…

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh
https://arxiv.org/abs/2506.14318

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-g…

Did some high res imaging of the standard cell area of the TPS54527 (and the TI logo in the corner).
Two scans, with focal planes roughly at M1 and M2.
https://siliconpr0n.org/map/ti/tps54527/azonenberg_roi1-fpm1_mit100x/

👉 Ihr könnt euch noch für den zweiteiligen Online-Workshop „Iconclass - Content Classification and Technical Features“ am Montag, 30.06. und Dienstag, 1.07. (jeweils 10 - 13 Uhr) anmelden!
💡 In Kooperation mit dem @… werden ausgewählte Use Cases präsentiert und die Möglichkeit zum Austausch angeboten, unter anderem in Breakout-Sessio…

Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Sara Capriotti, Alessio Devoto, Simone Scardapane, Silvano Mignardi, Laura Medeghini
https://arxiv.org/abs/2506.12250

Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petrographic analysis is time-consuming. This study explores the application of deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), as complementary tools to support the classification of Levantine ceramics based on their petrographic fabrics. A dataset of 1,424 thin sec…

At this year's Berlin Buzzwords, Viola Rädle & Raphael Franke will present gamma_flow, an open-source Python package for real-time spectral data analysis.
Learn more: https://2025.berlinbuzzwords.de/session/gamma_flow-deno…

Convexity in tensor triangular geometry
Changhan Zou
https://arxiv.org/abs/2506.12429 https://arxiv.org/pdf/2506.12429

Convexity in tensor triangular geometry
We classify the dualizable localizing ideals of rigidly-compactly generated tt-$\infty$-categories that are cohomologically stratified. By definition, these are the localizing ideals that are dualizable with respect to the Lurie tensor product. We prove that these ideals correspond to the convex subsets of the Balmer spectrum. More generally, we establish this classification for categories which are locally cohomologically stratified and whose Balmer spectrum is noetherian. The classification t…

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Yusdivia Molina-Rom\'an, David G\'omez-Ortiz, Ernestina Menasalvas-Ruiz, Jos\'e Gerardo Tamez-Pe\~na, Alejandro Santos-D\'iaz
https://arxiv.org/abs/2506.13964

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification a…