Oncology

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8 papers in the last four weeks, down 50% on the four weeks before. 0.1% of all new papers.

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CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction
Mar 3, 2025Rustin Soraki, Huayu Wang, Sitong Liu +2Computational Pathology Cancer Survival Prediction
University of Washington, Seattle, WA · University of California, Los Angeles, CA
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Mar 18, 2024Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed +4Healthcare Oncology
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA

Mar 3, 2025, eess.IVJ/K move · Enter open · S save

CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction

Rustin Soraki, Huayu Wang, Sitong Liu, Joann G. Elmore+1

University of Washington, Seattle, WA · University of California, Los Angeles, CA

Computational Pathology Cancer Survival Prediction Cross-Attention Multi-Scale Feature Fusion Oncology Whole-Slide Image Analysis

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Cancer survival prediction from whole slide images (WSIs) is a challenging task in computational pathology due to the large size, irregular shape, and high granularity of the WSIs. These characteristics make it difficult to capture the full spectrum of patterns, from subtle cellular abnormalities to complex tissue interactions, which are crucial for accurate prognosis. To address this, we propose CrossFusion, a novel multi-scale feature integration framework that extracts and fuses information from patches across different magnification levels. By effectively modeling both scale-specific patterns and their interactions, CrossFusion generates a rich feature set that enhances survival prediction accuracy. We validate our approach across six cancer types from public datasets, demonstrating significant improvements over existing state-of-the-art methods. Moreover, when coupled with domain-specific feature extraction backbones, our method shows further gains in prognostic performance compared to general-purpose backbones. The source code is available at: https://github.com/RustinS/CrossFusion

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Latest papers 212

CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure