arXiv AI

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

arXiv:2607. 20583v1 Announce Type: cross Abstract: The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely affected.

Hugging Face Trending Papers

Jul 3

An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification

Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning. Although deep learning has shown great potential for imaging signature discovery, its limited interpretability remains a major barrier to clinical adoption.

computer-vision safety

arXiv AI

Jul 7

An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification

arXiv:2607. 03593v1 Announce Type: cross Abstract: Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning.

By Chengkun Sun, Jinqian Pan, Renjie Liang, Zhengkang Fan, Xin Miao, Yi Guo, Mei Liu, Muxuan Liang, Russell Terry, Jie Xu

computer-vision safety

arXiv AI

Aug 5

OncoTriad-QA: A Patient-Level Radiology-Pathology-Genomics Benchmark for Pan-Cancer Reasoning

arXiv:2608. 02615v1 Announce Type: cross Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata.

By Ahnaf Munir, Dannong Wang, Michael W. McDonald, Mubarak Shah, Pegah Khosravi, Yu Tian

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 9

Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework

arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.

By Sarah de Boer, Hartmut H\"antze, Kiran Vaidhya Venkadesh, Myrthe A. D. Buser, Gabriel E. Humpire Mamani, Lina Xu, Lisa C. Adams, Jawed Nawabi, Keno K. Bressem, Bram van Ginneken, Mathias Prokop, Alessa Hering

computer-vision benchmarks

arXiv Machine Learning

Aug 11

PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning

arXiv:2608. 09721v1 Announce Type: new Abstract: Lung cancer remains one of the leading causes of cancer- related mortality worldwide.

By Mona Furukawa, Sai Hyne, Daniel R. McGowan, Bart{\l}omiej W. Papie\.z

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 10

OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib

arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.

By Abhijoy Sarkar, Aarchi Singh Thakur

llms benchmarks

arXiv Machine Learning

1d ago

AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions

arXiv:2608. 14778v1 Announce Type: cross Abstract: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from 70\%.

By Pranav Kulkarni, Nikhil Shah, Amritansh Suryavanshi, Jana Delfino, James Tonascia, Jade Wong-You-Cheong, Barton Lane, Joseph Chirico, Jeffrey D. Hirsch, Ang Li, Heng Huang, Florence X. Doo

benchmarks

arXiv AI

Jun 4

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.

By Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Jiang Bian, Russell Terry, Jie Xu

computer-vision

arXiv AI

Jul 10

SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.

By Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya, Benedikt Wiestler, Jana Lipkova

llms

arXiv Machine Learning

Jul 2

Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices

arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.

By Nils Neukirch, Martin Maurer, Nils Strodthoff

computer-vision fine-tuning benchmarks

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

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