PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

Whole Slide Image (WSI) analysis serves as the fundamental method for the clinical diagnosis of cancer in digital histopathology. Recent advances in Multimodal Large Language Models (MLLMs) have enabled state-of-the-art (SOTA) automated histopathology report generation. However, MLLMs reason purely through distributions encoded in their neural parameters, limiting the amount of knowledge and reasoning logic they can contain given the model scale. Their black-box reasoning poses another challenge to the human interpretation of intermediate reasoning. We address these issues by utilizing knowledge graphs to inject external structured knowledge for MLLMs to reason over, while making this reasoning component visible. Knowledge graphs are capable of partially externalizing the containment of medical knowledge, an area that remains underexplored in pathology report generation. We introduce PathKGen, a knowledge graph-assisted MLLM for histopathology report generation. PathKGen has two main branches: (1) a visual path that learns visual token inputs to the MLLM using pathology foundation models, and (2) a knowledge path that learns knowledge token inputs by extracting key morphological features of WSIs through querying a pathology-specific knowledge graph. A comprehensive evaluation on WSI-Bench dataset shows that PathKGen surpasses previous SOTA methods while revealing diagnostic reasoning as visually intuitive graphs. Due to its modular design, the proposed knowledge path is complementary to vision-aligned MLLMs and can be extended to other applications by curating domain-specific knowledge graphs to generate knowledge tokens.

Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing Computational Pathology (CPath) Multimodal Large Language Models (MLLMs) typically compress an entire WSI into a single embedding, which hinders fine-grained grounding and ignores how pathologists synthesize evidence across different scales. We introduce MLLM-HWSI, a Hierarchical WSI-level MLLM that aligns visual features with pathology language at four distinct scales, cell as word, patch as phrase, region as sentence, and WSI as paragraph to support interpretable evidence-grounded reasoning. MLLM-HWSI decomposes each WSI into multi-scale embeddings with scale-specific projectors and jointly enforces (i) a hierarchical contrastive objective and (ii) a cross-scale consistency loss, preserving semantic coherence from cells to the WSI. We compute diagnostically relevant patches and aggregate segmented cell embeddings into a compact cellular token per-patch using a lightweight Cell-Cell Attention Fusion (CCAF) transformer. The projected multi-scale tokens are fused with text tokens and fed to an instruction-tuned LLM for open-ended reasoning, VQA, report, and caption generation tasks. Trained in three stages, MLLM-HWSI achieves new SOTA results on 13 WSI-level benchmarks across six CPath tasks. By aligning language with multi-scale visual evidence, MLLM-HWSI provides accurate, interpretable outputs that mirror diagnostic workflows and advance holistic WSI understanding. Code is available at: GitHub.

Multimodal large language models (MLLMs) are increasingly used to analyze pathology images. However, dominant multimodal benchmarks in pathology mainly score final diagnostic answers, captions, or reports. These evaluations provide limited insight into whether a model understands the multiscale visual content needed for pathology reasoning and decision-making. We introduce PathVU, a vision-anchored benchmark for fine-grained and multiscale visual understanding in computational pathology. Built from 23 public pathology imaging datasets with human-supervised labels and spatial annotations, PathVU evaluates MLLM understanding in two fields of view: Region FOV for high-resolution local regions and Slide FOV for macro whole-slide views. By converting raw annotations into deterministic task targets, PathVU enables programmatic scoring of region localization, visual recognition, quantity estimation, spatial reasoning, and insufficient-context judgment. The benchmark contains 14 VQA-style tasks, 61,673 images, and 308,070 samples across 28 organs and 7,253,526 annotations. Evaluating 18 representative general-purpose, medical-domain, and pathology-oriented MLLMs, we observe substantial limitations even in advanced models on fine-grained visual tasks across multiscale pathology images. PathVU provides a reproducible basis for developing and evaluating pathology MLLMs with explicit multiscale visual understanding.