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Deep convolutional neural networks are widely used in structural magnetic resonance imaging (sMRI) analysis for the early diagnosis of Alzheimer's disease. To address the challenge of efficient representation learning in sMRI, this study proposes a two-pathway convolutional network that improves the computational efficiency of sMRI feature extraction by representation decoupling, and further strengthens the representation discriminability through adaptive feature fusion. The network consists of three parts：1) A high-channel-capacity slice path, which processes sparse slices to encode semantic information of slice images；2) A low-channel-capacity context path, which processes dense slices to capture inter-slice contextual information；3) An adaptive feature fusion module, which integrates the decoupled information from both paths to generate more effective sMRI representations. The proposed method was evaluated on two tasks—Alzheimer's disease classification and mild cognitive impairment conversion prediction—using the Alzheimer's disease neuroimaging initiative (ADNI) dataset. The results demonstrate that the proposed approach surpasses the baseline models in both computational efficiency and diagnostic performance, while achieving results comparable to those of current state-of-the-art methods.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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