Table 7: We present the prediction accuracies of BiomedGPT when dealing with high-resolution medical images.
Our report showcases the superior average results yielded by a single model on these two datasets, as documented
in the existing literature. Moreover, the implementation of a voting-based ensemble method, as described in (Tasci
et al., 2021), enhances the performance even further, achieving impressive results in excess of 95% for both datasets.
For this analysis, the Inception v3 model was pre-trained.

Model
BiomedGPTSmall
BiomedGPTMedium
BiomedGPTBase
Inception v3 (Szegedy et al., 2016)
+ CLAHE (Pizer, 1986; Pizer et al., 1987)
+ RandXScale

MC-CXR SZ-CXR

75.86
82.76
89.65
67.85
75.00
92.85

83.46
96.99
96.24
84.96
87.61
87.61

B.3 Lightweight Prompt Tuning
BiomedGPT