Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation

Table 3 Taxonomy of deep learning architectures for hepatocellular carcinoma tasks: Mapping to data types and efficiency profiles

Primary task	Data modality	Suitable DL architectures	Efficiency profile & trade-offs
Lesion detection & diagnosis	Static US/CT/MRI slice	Lightweight CNNs (MobileNet, EfficientNet), standard CNNs	Pareto-efficient: Lightweight CNNs are optimized for high speed and low cost with minimal accuracy loss. Standard CNNs offer a balance
	Volumetric CT/MRI	3D CNNs, 2.5D CNNs (processing slices sequentially)	Trades speed for accuracy: 3D CNNs are accurate but computationally heavy. 2.5D/pseudo-3D approaches are a more efficient compromise
	Multi-phase CT/MRI	Multi-input/weight-sharing CNNs, transformer-CNN hybrids	Pareto-efficient: Multi-input CNNs efficiently fuse phase data. Hybrids use transformers to capture long-range dependencies between phases without a full transformer's cost
Segmentation	CT/MRI volumes	U-Net variants, transformer-based (e.g., UNETR)	Trades speed for accuracy: U-Nets are relatively efficient. Pure transformers (e.g., UNETR) offer superior accuracy for complex shapes but are computationally intensive
Longitudinal risk prediction	Tabular EHR time-series	RNNs (LSTM/GRU), transformers, lightweight ML	Context-dependent: RNNs/transformers model time well but can be heavy. For simpler tasks, logistic regression/gradient boosting (non-DL) are often more efficient and perform similarly
Histopathology classification	WSI	MIL + CNN, ViT	Pareto-efficient: MIL frameworks are inherently efficient, processing bags of image patches. New efficient ViT variants are emerging for WSI analysis
Multimodal fusion	Fused imaging + clinical	Hybrid architectures (e.g., CNN branch for images, DNN for tabular data)	Pareto-efficient: Hybrid designs effectively integrate data types. The efficiency is determined by the choice of the image backbone (e.g., using a lightweight CNN)
High-precision detection	CT/MRI (small lesions)	Dense detectors, ViT	Trades speed for accuracy: Models like ViT and complex detectors excel at finding small lesions due to global attention but have high computational demands

US: Ultrasonography; MRI: Magnetic resonance imaging; CT: Computed tomography; CNN: Convolutional neural network; LSTM: Long short-term memory; ML: Machine learning; WSI: Whole slide image; ViT: Vision transformers; MIL: Multiple instance learning; UNETR: UNEt transformers; EHR: Electronic health record; GRU: Gated recurrent units; DNN: Deep neural network; DL: Deep learning; RNNs: Recurrent neural networks; CNNs: Convolutional neural networks.