FCMNet-RA: Fuzzy cognitive mimicking network with reasoning agent for histopathology-based cancer diagnosis

In the cognitive process of histopathology-based cancer diagnosis, pathologists form an initial fuzzy abnormality judgment through global visual perception, and then perform detailed multi-scale evaluation, which leads to diagnosis under the guidance of potential disease associations. However, existing diagnosis methods rarely formalize this pathologist-inspired process, which limits diagnostic-process transparency and predictive reliability. Consequently, we propose a fuzzy cognitive mimicking network with reasoning agent (FCMNet-RA) for histopathology-based cancer diagnosis. The network constructs a fuzzy abnormality perception module to model the initial fuzzy abnormality judgment. The module extracts multi-granularity information from different stages and quantifies abnormal cues through fuzzy membership functions, thereby forming fuzzy perception of potential abnormal regions. To further organize multi-scale evaluation, the network constructs a stage-wise deliberative reasoning agent. The reasoning agent designs stage adapters and a cognition-sharing subnetwork for unified evaluation, and then regulates the contribution of each stage to the final diagnosis according to fuzzy abnormality cues and stage information. Moreover, a cognitive prototype calibration mechanism is designed to incorporate the guidance of potential disease associations. The mechanism constructs disease cognitive prototypes with diagnostic logic priors to encode potential category associations inspired by pathological diagnosis, thereby guiding different network stages toward consistent representations aligned with the final diagnosis. Overall, FCMNet-RA adopts a pathologist-inspired process design to improve diagnostic-process transparency and predictive reliability. Comprehensive experiments on two private clinical datasets and two public datasets demonstrate that FCMNet-RA outperforms existing diagnosis methods, while qualitative and quantitative analyses provide further support for diagnostic-process transparency and predictive reliability.