| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T436 |
0-45 |
Sentence |
denotes |
The workflow of the classification framework. |
| T437 |
46-119 |
Sentence |
denotes |
The workflow of the classification framework was demonstrated in Fig. 3c. |
| T438 |
120-248 |
Sentence |
denotes |
The preprocessed images are sent to the first convolution block to expand the channels and processed as the input for the CNNCF. |
| T439 |
249-391 |
Sentence |
denotes |
Given the input Fi with a size of M × N × 64, the stage I output feature maps F′i with a size of M/8 × N/8 × 256 in the default configuration. |
| T440 |
392-531 |
Sentence |
denotes |
As we introduced above, the Control Gate Block controls the optimization direction while controlling the information flow in the framework. |
| T441 |
532-614 |
Sentence |
denotes |
If the Control Gate Block is open, the feature maps F′i are passed on to stage II. |
| T442 |
615-920 |
Sentence |
denotes |
Given the input F′i, the stage II output the feature maps F″i with a size of M/64 × N/64 × 512 which is defined as follows:1 Fi′=S1(Fi)Fi″=S2(Fi′)⊗CGB(Fi′),where S1 denotes the stage I block, S2 denotes the stage II block, and CGB is the Control Gate Block. ⊗ is the element-wise multiplication operation. |
| T443 |
921-1077 |
Sentence |
denotes |
Stage II is Followed by a global average pooling layer (GAP) and a fully connect layer (FC layer) with a softmax function to generate the final predictions. |
| T444 |
1078-1168 |
Sentence |
denotes |
Given F″i as input, the GAP is adopted to generate a vector Vf with a size of 1 × 1 × 512. |
| T445 |
1169-1536 |
Sentence |
denotes |
Given Vf as input, the FC layer with the softmax function outputs a vector Vc with a size of 1 × 1 × C.2 Vf=GAPFi′Vc=SMaxFCVf,where GAP is the global average pooling layer, the FC is the fully connect layer, SMax is the softmax function, Vf is the feature vector generated by the GAP, Vc is the prediction vector, and C is the number of case types used in this study. |
