Generalizability of convolutional neural network‐based model observer in breast tomosynthesis across volume glandular fractions and signal sizes

Networks trained on datasets sharing similarly sized signals exhibited optimal detection performance, but showed limited generalizability when applied to datasets with signals of different sizes, while those trained on datasets involving signals of both sizes resulted in better generalizability. This work suggests that task-based exploration is crucial for designing CNN-based model observers that can perform and generalize consistently, providing valuable guidance for developing robust networks for varying imaging scenarios.