Hugging Face Trending Papers

StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

Read the original on Hugging Face Trending Papers →

Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research environments due to, among other reasons, software dependencies and computational requirements. We introduce StrokeSeg2, a lightweight, modular, cross-platform C++/Qt framework designed to adapt resource-intensive 3D stroke segmentation pipelines into portable and reproducible applications.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI
Aug 3

OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation

arXiv:2607. 29266v1 Announce Type: cross Abstract: Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise.

By Maximo Rodriguez-Herrero, Dante D. Sanchez-Gallegos, Heriberto Aguirre-Meneses, Marco Antonio N\'u\~nez-Gaona, J. L. Gonzalez-Compean, Jesus Carretero
Hugging Face Trending Papers
Jun 24

Energy-Efficient CNN Acceleration with MSDF Digit-Serial Arithmetic on FPGA

This paper presents an energy-efficient hardware acceleration of the convolutional layers in the U-Net architecture for image segmentation, implemented on FPGA. While digit-serial arithmetic, particularly most-significant-digit-first (MSDF) techniques, offers a compact hardware footprint, it suffers from initial latency before producing the first output digit.

arXiv AI
Jul 21

SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs

arXiv:2607. 18081v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across a range of Natural Language Processing (NLP) tasks, but their high computational and memory demands pose significant challenges for deployment on resource-constrained edge devices.

By Huzaifa Shaaban Kabakibo, Eric Schniedermeyer, Artem Burchanow, Lin Wang
arXiv AI
Jul 24

Hardware-Software Co-Design for Float16 On-Device Training on RISC-V Single-Core

arXiv:2607. 21130v1 Announce Type: cross Abstract: By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framework to enable complete on-device training on resource-constrained RISC-V single-core.

By Benjamin Hubinet, Pierre-Alain Moellic, Olivier Savry, Olivier Potin, Jean-Baptiste Rigaud