Zhikang Wang

Jingyun Chen, Linghan Cai, Zhikang Wang et al.

Analyzing whole-slide images (WSIs) requires an iterative, evidence-driven reasoning process that parallels how pathologists dynamically zoom, refocus, and self-correct while collecting the evidence. However, existing computational pipelines often lack this explicit reasoning trajectory, resulting in inherently opaque and unjustifiable predictions. To bridge this gap, we present PathAgent, a training-free, large language model (LLM)-based agent framework that emulates the reflective, stepwise analytical approach of human experts. PathAgent can autonomously explore WSI, iteratively and precisely locating significant micro-regions using the Navigator module, extracting morphology visual cues using the Perceptor, and integrating these findings into the continuously evolving natural language trajectories in the Executor. The entire sequence of observations and decisions forms an explicit chain-of-thought, yielding fully interpretable predictions. Evaluated across five challenging datasets, PathAgent exhibits strong zero-shot generalization, surpassing task-specific baselines in both open-ended and constrained visual question-answering tasks. Moreover, a collaborative evaluation with human pathologists confirms PathAgent's promise as a transparent and clinically grounded diagnostic assistant.

Fengchun Liu, Linghan Cai, Zhikang Wang et al.

Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to precision medicine. However, a significant challenge in clinical practice arises when only unimodal data is available, limiting the usability of these advanced multimodal methods. To address this issue, this study proposes a prototype-guided cross-modal knowledge enhancement (ProSurv) framework, which eliminates the dependency on paired data and enables robust learning and adaptive survival prediction. Specifically, we first introduce an intra-modal updating mechanism to construct modality-specific prototype banks that encapsulate the statistics of the whole training set and preserve the modality-specific risk-relevant features/prototypes across intervals. Subsequently, the proposed cross-modal translation module utilizes the learned prototypes to enhance knowledge representation for multimodal inputs and generate features for missing modalities, ensuring robust and adaptive survival prediction across diverse scenarios. Extensive experiments on four public datasets demonstrate the superiority of ProSurv over state-of-the-art methods using either unimodal or multimodal input, and the ablation study underscores its feasibility for broad applicability. Overall, this study addresses a critical practical challenge in computational pathology, offering substantial significance and potential impact in the field.