arXiv Machine Learning August 18, 2026 By Eve Harling (James Watt School of Engineering, College of Science & Engineering, University of Glasgow, Glasgow, UK), Chattarin Pumtako (Academic Unit of Surgery, School of Medicine, College of Medical Veterinary & Life Sciences, University of Glasgow, Glasgow, UK), Bernd Porr (James Watt School of Engineering, College of Science & Engineering, University of Glasgow, Glasgow, UK), Donald C McMillan (Academic Unit of Surgery, School of Medicine, College of Medical Veterinary & Life Sciences, University of Glasgow, Glasgow, UK), Ross D Dolan (Academic Unit of Surgery, School of Medicine, College of Medical Veterinary & Life Sciences, University of Glasgow, Glasgow, UK)

Deep learning-based computed tomography (CT) derived body composition classifier for colorectal cancer patients

Read the original on arXiv Machine Learning →

arXiv:2608. 15712v1 Announce Type: cross Abstract: Background: Accurate body composition analysis using Computed Tomography (CT) scans is essential for assessing skeletal muscle area (SMA) and skeletal muscle density (SMD), key markers of nutritional status in cancer patients.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

computer-vision

arXiv AI

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Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

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arXiv AI

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Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

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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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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.

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arXiv Machine Learning

Jun 16

A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.

By Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel

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