Cross-modal attentive fusion network for tri-modal lesion growth prediction.

Revathi Appavoo, Vimaladevi Madhivanan, Abhay Chaturvedi, Janjhyam Venkata Naga Ramesh, Divya Nimma, V Ravi Kishore

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Revathi AppavooDepartment of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, India. arevathiprakash@gmail.com.

Vimaladevi MadhivananDepartment of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.

Abhay ChaturvediDepartment of Electronics and Communication Engineering, GLA University, Mathura, PIN-281406, Uttar Pradesh, India.

Janjhyam Venkata Naga RameshDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist, Andhra Pradesh, 522302, India.

Divya NimmaArkansas Tech University , 215 West O Street , Russellville, Arkansas , 72801, USA.

V Ravi KishoreDepartment of Computer Science & Engineering, Aditya University, Surampalem, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pre-trained LSTM-RNN models with linear kernels fail to capture irregular, non-linear lesion growth patterns. In this paper presents a new deep learning model is called Cross-Modal Attention Fusion Network (CMAFN) to enhance an effective feature fusion of multiple medical image modalities such as Mammography, Magnetic Resonance Imaging (MRI) and Ultrasound (US). It enables progressively transforms and aligns features from multiple modalities into a unified representation space. Our CMAFN model combines the three advanced modules, Deep Canonical Correlation Analysis (DCCA) to effectively fuses non-linear features in imaging modalities, Cross-Modal Attention Mechanism (CMAM) to adaptively combine modalities and align features, and Radial Basis Function with Conventional Long Term Short Memory (RBF-ConvLSTM) to learn both linear and non-linear spatial-temporal growth patterns. Overall, the proposed CMAFN model supportive for multi-modal medical image analysis and interpretable predictions of lesion progression over time.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedAlgorithmsHumansLong Short Term MemoryMagnetic Resonance ImagingMammographyUltrasonographyCross-modal attention weightsDisease growth prediction and Computer-aided diagnosisMedical image analysisMulti-modal feature fusion

Identifiers

PMID42259863

DOI10.1038/s41598-026-53402-w

PMCPMC13487311

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