arXiv AI

A Computationally Efficient Multi-Section Whole Slide Image Analysis for Capturing Prostate Cancer Multifocality in Biochemical Recurrence Prediction

The study introduces a computationally efficient multi‑section whole slide image analysis framework that captures the entire prostate tumor landscape to predict biochemical recurrence (BCR) after radical prostatectomy. Using a large dataset of 23,451 slides from 789 patients, the authors optimized patch and slide subsampling to reduce computational demands while achieving strong predictive performance for 1‑ and 2‑year BCR, comparable to or better than existing clinical benchmarks. The AI‑derived risk score was an independent prognostic factor, improved prognostic performance when combined with clinical variables, and showed preliminary transferability in an external cohort, with attention‑dispersion analysis confirming that the model leveraged information from multiple sections consistent with prostate cancer’s multifocal nature.

Hugging Face Trending Papers

Jul 15

TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance

Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.

computer-vision multimodal

arXiv Computer Vision

Aug 25

CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets

arXiv:2608.21497v1 Announce Type: cross Abstract: Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely us...

By Robert N. Spaans, Catherine Chia, Tongjie Wang, Adam Kowalewski, Parandzem Khachatryan, Domingos Oliveira, Khrystyna Faryna, Jean-Paul A. van Basten, Geert Litjens, Nadieh Khalili

multimodal benchmarks

arXiv Machine Learning

Jul 9

Compass: Prostate Cancer Detection Needs Multi-View Context

arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.

By Paul F. R. Wilson, Mohamed Harmanani, Zhuoxin Guo, Obed K. Dzikunu, Hannes Cash, Adam Kinnaird, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi

llms

arXiv AI

Sep 1

Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling

arXiv:2606.28676v2 Announce Type: replace-cross Abstract: Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational patholog...

By Sandesh Pokhrel, Hamid Manoochehri, Beatrice S Knudsen, Tolga Tasdizen

rag

arXiv AI

1d ago

Multimodal Fusion Methods for Whole-Slide Images and Clinical Data in Inflammatory Bowel Disease

The paper introduces a modular framework for multimodal AI fusion that combines slide-level embeddings from histology images with per-visit clinical embeddings using 15 interchangeable fusion methods. Applied to a prospective cohort of newly diagnosed inflammatory bowel disease patients, the best fusion method achieved macro‑F1 scores between 0.57 and 0.75, outperforming single‑modality models in most scenarios and showing varying reliance on image data depending on the remission outcome. The study highlights how different fusion strategies can influence predictive performance across distinct clinical endpoints.

By Ankana Banerjee (University of Stavanger, Norway), Mark van der Giezen (University of Stavanger, Norway), Ole Gunnar Aasprong (Stavanger University Hospital, Norway), Emiel A. M. Janssen (University of Stavanger, Norway), {\O}yvind Steinsb{\o} (Stavanger University Hospital, Norway), Tore Grimstad (Stavanger University Hospital, Norway), Kjersti Engan (University of Stavanger, Norway)

rag multimodal

arXiv AI

Aug 21

MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation

arXiv:2510. 17529v3 Announce Type: replace-cross Abstract: Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up.

By Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ourselin, Michela Antonelli

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 29

Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC

arXiv:2606. 27579v1 Announce Type: cross Abstract: Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis.

By Krzysztof Pysz, Artur Bartczak, Jaros{\l}aw Kwiecie\'n, Piotr Krajewski, Witold Dyrka

rag

arXiv Computer Vision

Sep 1

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction

arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...

By Shuo Jiang, Zhuwen Chen, Liaoman Xu, Yanming Zhu, Changmiao Wang, Jiong Zhang, Feiwei Qin, Yifei Chen, Zhu Zhu

multimodal safety

arXiv Computer Vision

Sep 2

SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling

arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...

By Chad Wong, Sicheng Chen, Tianyi Zhang, Enhui Chai, Yueming Jin, Zeyu Liu, Fei Xia

llms rag multimodal

arXiv Machine Learning

Sep 3

Morphology signal in whole slide image foundation models can automatically triage slides

The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.

By Ayushi Sinha, Shashank Yadav, Benjamin Holmes, Pravat Das, Aaron W. Bogan, James S. Lewis Jr., Santiago Romero-Brufau, Andrew Y. K. Foong, Scott H. Kaufmann, Kathryn M. Van Abel, David M. Routman, Michael R. Lucas

benchmarks

arXiv Machine Learning

Jul 24

Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction

arXiv:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.

By Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson, Mattias Rantalainen

arXiv Computer Vision

Oct 7

Anchor-driven Multi-modal Multi-scale Expert Selection for Survival Prediction

arXiv:2610.07694v1 Announce Type: new Abstract: The integrative analysis of histopathological Whole-Slide Images (WSIs) and transcriptomic profiles holds significant promise for cancer survival predi...

By Tao Zhou, Ying Hu, Huazhu Fu, Yi Zhou, Xiao-Jun Wu, Haibin Ling

diffusion multimodal benchmarks safety

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