From: Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence
Source | Sensitivity | Specificity | Accuracy | AUC |
---|---|---|---|---|
Dataset-A (patch-level testing) | ||||
XH | 96.99% | 99.22% | 98.11% | 99.83% |
Dataset-B (patch-level validation) | ||||
NCT-CRC-HE-100 K | 92.03% | 96.74% | 96.07% | 98.32% |
CRC-VAL-HE-7 K | 94.24% | 94.87% | 94.76% | 98.45% |
Dataset-C (patient-level validation) | ||||
XH | 98.80% | 99.51% | 99.02% | 99.16% |
TCGA-Frozen | 94.04% | 88.06% | 93.44% | 91.05% |
TCGA-FFPE | 97.96% | 100.00% | 97.98% | 98.98% |
SYU-CGH | 98.90% | 92.45% | 95.43% | 95.68% |
Dataset-D (patient-level Human-AI contest) | ||||
XH | 97.96% | 100% | 98.97% | 98.99% |
SYU | 98.90% | 100% | 98.97% | 99.45% |
Dataset-C and Dataset-D (patient-level validation and Human-AI contest) | ||||
PCH | 96.00% | 97.83% | 96.88% | 97.91% |
TXH | 100% | 97.92% | 98.96% | 99.20% |
HPH | 97.96% | 97.96% | 97.96% | 98.98% |
FUS | 100% | 97.96% | 98.99% | 99.99% |
GPH | 100% | 97.65% | 98.91% | 99.15% |
NJD | 92.93% | 97.94% | 95.41% | 95.84% |
SWH | 98.99% | 97.00% | 97.99% | 99.42% |
AMU | 97% | 97.06% | 97.04% | 98.37% |
ACL | 100% | 97.20% | 98.55% | 99.83% |