arXiv Machine Learning July 31, 2026 By Katy L. Scott, Sejin Kim, Joshua Siraj, Caryn Geady, Matthew Boccalon, Mattea Welch, Mogtaba Alim, Andrew J. Hope, Benjamin Haibe-Kains

Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features

Read the original on arXiv Machine Learning →

arXiv:2607. 28423v1 Announce Type: cross Abstract: Radiomics and imaging foundation models promise non-invasive biomarkers of tumour biology, yet predictive signatures may reflect tumour volume or acquisition artifacts rather than meaningful image structure.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 10

ConRad: Efficient Conformal Prediction for Radiomics

arXiv:2607. 08084v1 Announce Type: cross Abstract: Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines.

By Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan

computer-vision benchmarks

arXiv Computer Vision

Sep 23

Complementary Roles of Radiomics and Foundation Representations in Renal Cell Carcinoma Classification: A Comparative Study of 2D and 3D CT Encodings

arXiv:2609.26463v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced computed tomography remains clinically challenging. R...

By Yuan Liang, Sourav Bhattacharjee, Abraham Campbell

benchmarks safety

arXiv Computer Vision

Sep 23

Radiomics--Foundation Fusion for Interpretable RCC Classification: Internal Benchmarking and Exploratory External Transfer

arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...

By Yuan Liang, Fangyijie Wang, Kathleen M. Curran, Gu\'enol\'e Silvestre, Sourav Bhattacharjee, Abraham Campbell

benchmarks safety

arXiv Computer Vision

Sep 1

SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer

arXiv:2608.29153v1 Announce Type: new Abstract: Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation mo...

By Ayush Gupta, Vinkle Srivastav, Prateek Upadhya, Amit Gupta, Krithika Rangarajan, Nicolas Padoy

safety

arXiv Machine Learning

Jun 4

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

arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.

By Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon

multimodal

arXiv AI

Jul 8

CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training

arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

One email a morning, machine-written

Related stories

ConRad: Efficient Conformal Prediction for Radiomics

Complementary Roles of Radiomics and Foundation Representations in Renal Cell Carcinoma Classification: A Comparative Study of 2D and 3D CT Encodings

Radiomics--Foundation Fusion for Interpretable RCC Classification: Internal Benchmarking and Exploratory External Transfer

SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer

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

CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training