Prostate Cancer Grading

Latest papers

11 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Multi-scale Fusion of Fast Vision Mamba and Foundation Model Features for Patch-Level Gleason Grading in Computational Pathology
Oct 19, 2026Zihe Zhang, Weimiao Yu, Piumi Don SimongeHistopathology Image Classification Prostate Cancer Grading
Nanyang Technological University, Singapore · Bioinformatics institute, Agency for Science Technology and Research, Singapore
Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading
Sep 14, 2026Obed Korshie Dzikunu, Emma Willis, Mohammad Mahdi Abootorabi +7Computational Pathology Cross-Modal Knowledge Distillation
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada · Vector Institute, Toronto, Canada · School of Computing, Queen’s University, Kingston, Canada +3
An end-to-end-trained vision-language model for native-language prostate pathology report generation
Aug 24, 2026Christian Grashei, Fabian Gülhan, Maximilian Legnar +4Prostate Cancer Grading Medical Report Generation
Institute of Pathology, Technical University of Munich, Munich, Germany · Munich Data Science Institute, Munich, Germany · Munich Center for Machine Learning, Munich, Germany +2
Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels
Jul 26, 2026Nao Sugeta, Kaito Shiku, Shinnosuke Matsuo +1Weakly Supervised Learning Oncology
Kyushu University, Japan
Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
Jul 16, 2026Yipei Wang, Shiqi Huang, Wen Yan +6Causal Representation Learning Medical Image Classification
UCL Hawkes Institute, University College London, London, UK · Department of Medical Physics and Biomedical Engineering, University College London, London, UK · Department of Urology, University College London Hospital, London, UK +4
ProsMAE: Multi-Source MAE Pretraining for ISUP Grade Classification
Jul 9, 2026Anna Jung, Kyeonghun Kim, Youngung Han +6Computational Pathology Masked Autoencoders
SNU · OUTTA · GIST +1
KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning
Jul 7, 2026Zheng Guo, Jiaqi Cui, Haocheng Xiong +5Medical Image Classification Ordinal Regression
School of Computer Science, Sichuan University, China · China Mobile (Suzhou) Software Technology Company Limited, China · Shanghai Changhai Hospital, Naval Medical University, China +1
Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples
May 4, 2026Xiaoyi Ji, Renata Zelic, Oskar Aspegren +11Computational Pathology Domain Generalization
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden · Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden · Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden +8
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
Mar 10, 2026Marie Arrivat, Rémy Peyret, Elsa Angelini +1Prostate Cancer Grading Histopathology Image Classification
Primaa, Paris, France · LTCI, Telecom Paris, Institut Polytechnique de Paris, France
Adaptive Prototype-based Interpretable Grading of Prostate Cancer
Mar 5, 2026Riddhasree Bhattacharyya, Pallabi Dutta, Sushmita MitraProstate Cancer Grading Histopathology Image Classification
Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108
GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound
Feb 22, 2026Emma Willis, Tarek Elghareb, Paul F. R. Wilson +6Prostate Cancer Grading Medical Image Classification
Queen’s University, Kingston, Canada. · University of British Columbia, Vancouver, Canada∗. · Exact Imaging, Markham, Canada.

Oct 19, 2026J/K move · Enter open · S save

Multi-scale Fusion of Fast Vision Mamba and Foundation Model Features for Patch-Level Gleason Grading in Computational Pathology

Zihe Zhang, Weimiao Yu, Piumi Don Simonge

Nanyang Technological University, Singapore · Bioinformatics institute, Agency for Science Technology and Research, Singapore

Histopathology Image Classification Prostate Cancer Grading Multi-Scale Feature Fusion Pathology Foundation Models

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Automated Gleason grading of prostate whole slide images (WSIs) enables faster and more consistent cancer risk stratification. However, it remains a challenging task due to the subtle morphological differences between grades, the severe class imbalance arising from rare patterns, and the diagnostic features that span multiple spatial scales. Pathology foundation models (FMs) extract strong patch representations but often operate at a single-scale, which limits spatial context and disproportionately affects classes that depend on broader context. The challenge of fusing multiple scales and complementary models, with minimal data and computational resources, remains unresolved. We propose a multi-scale classifier built on state-space models. Our method combines two complementary feature sources: a frozen foundation model, and a lightweight FastVim, pre-trained on ImageNet and distilled from the foundation model, that reads the raw image crops. At each scale, a Mamba model encodes the features from both sources with linear-time spatial context, and a jointly trained head fuses the encoded representations into one prediction. On the PANDA dataset, our method reaches macro F1 of 0.894 and Gleason 5 F1 of 0.802 under 5-fold cross-validation, a 12% and 21% relative improvement over the best single-scale model. A controlled study over 5 foundation models, 5 base classifiers and 3 scales confirms that the gain holds across encoders, with larger improvements observed at smaller scales.