A Multi-Phase Multi-Graph Approach for Focal Liver Lesion Classification on CT Scans

Tran Bao Sam, Ta Duc Huy, Cong Tuyen Dao, Thanh Tin Lam, Van Ha Tang, Steven Q.H. Truong; Proceedings of the Asian Conference on Computer Vision (ACCV), 2024, pp. 3788-3802

Abstract

Liver cancer remains a leading cause of global mortality, driving interest in computer-aided diagnosis for liver tumor detection. Existing methods typically focus on individual lesions and avoid the impact of neighboring tumors on diagnostic accuracy. This study introduces a novel multi-phase multi-graph (MPMG) approach to improve liver tumor classification using contrast-enhanced computed tomography (CECT) scans. The MPMG method models inter-lesion relationships, including the ratio of diameters, semantic similarity, physical distance, and neighbor influence score as graph edge embeddings, while multiphasic features extracted from a proposed deep convolutional neural network form the node representations. By analysing different edge embedding formations, we find through extensive experiments that the proposed MPMG model outperforms several state-of-the-art methods in liver tumor diagnosis.

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[bibtex]
@InProceedings{Sam_2024_ACCV, author = {Sam, Tran Bao and Huy, Ta Duc and Dao, Cong Tuyen and Lam, Thanh Tin and Tang, Van Ha and Truong, Steven Q.H.}, title = {A Multi-Phase Multi-Graph Approach for Focal Liver Lesion Classification on CT Scans}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2024}, pages = {3788-3802} }