Hugging Face Trending Papers July 28, 2026

Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer

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Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability.

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arXiv Machine Learning

Jun 4

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

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arXiv AI

Aug 17

CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification

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rag multimodal safety

Hugging Face Trending Papers

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Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models.

multimodal

arXiv Computer Vision

Sep 23

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benchmarks safety

arXiv AI

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By Fakrul Islam Tushar, Stephen Adamo, Geoffrey D. Rubin

rag

arXiv Machine Learning

Jul 2

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By Nils Neukirch, Martin Maurer, Nils Strodthoff

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