Table 2 Properties and benefits of different fusion strategies.

From: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

 

Early

Joint

Late

Able to make predictions when not all modalities are present

×

×a

✓

Able to model interactions between features from different modalities

✓

✓

×

Able to learn more compatible features from each modality

×

✓

×

Does not necessarily require a large amount of training data

×

×

✓

Does not require training multiple models

✓b

✓

×

Does not necessarily require meticulous designing efforts

✓

×

✓

Flexibility to join input at different levels of abstraction

×

✓

×

  1. Different properties and benefits for each fusion strategy.
  2. aSpecialized joint fusion architecture such as Kawahara et al.’s multi-modal multi-task model is capable of handling missing data.
  3. bEarly fusion requires training of multiple models when the imaging features are extracted using CNN.