REVIEW 8 cited by
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with language context for comprehensive diagnostic analysis. These models hold particular promise for automating complex tasks that traditionally require expert interpretation of pathologists. However, current MLLM approaches in pathology demonstrate significantly constrained reasoning capabilities, primarily due to their reliance on expensive chain-of-thought annotations. Additionally, existing methods remain limited to simplex application of visual question answering (VQA) at the region-of-interest (ROI) level, failing to address the full spectrum of diagnostic needs such as ROI classification, detection, segmentation, whole-slide-image (WSI) classification and VQA in clinical practice. In this study, we present SmartPath-R1, a versatile MLLM capable of simultaneously addressing both ROI-level and WSI-level tasks while demonstrating robust pathological reasoning capability. Our framework combines scale-dependent supervised fine-tuning and task-aware reinforcement fine-tuning, which circumvents the requirement for chain-of-thought supervision by leveraging the intrinsic knowledge within MLLM. Furthermore, SmartPath-R1 integrates multiscale and multitask analysis through a mixture-of-experts mechanism, enabling dynamic processing for diverse tasks. We curate a large-scale dataset comprising 2.3M ROI samples and 188K WSI samples for training and evaluation. Extensive experiments across 72 tasks validate the effectiveness and superiority of the proposed approach. This work represents a significant step toward developing versatile, reasoning-enhanced AI systems for precision pathology.
Forward citations
Cited by 8 Pith papers
-
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open ...
-
Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment
DMCoStain iteratively refines stain-transfer training data by using a pathology-savvy vision-language model to select high-quality generated IHC images, achieving state-of-the-art H&E-to-IHC translation on six biomarkers.
-
Enhancing Pathological VLMs with Cross-scale Reasoning
Presents Scale-VQA benchmark for cross-scale pathology VQA and RL-trained ScaleReasoner-R1 model that reaches SOTA on the new benchmark plus existing single-scale tasks.
-
Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
A new open pipeline and dataset enable training of a vision-language model for whole-slide pathology VQA that outperforms MedGemma on tissue identification, neoplasm detection, and differential diagnosis.
-
Enhancing Pathological VLMs with Cross-scale Reasoning
Cross-scale supervision from a leakage-curated multi-magnification VQA benchmark improves pathology VLMs on both multi-image and single-image evaluation.
-
A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology
PathPocket constructs a 4.55M-entity pathology hypergraph from 110k graded documents and deploys a multi-agent framework that outperforms prior systems on 200k cases while raising pathologist accuracy in user studies.
-
EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification
EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TC...
-
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
PathoSage is a three-stage framework using Structured Evidence Deliberation and a Beta-Bernoulli experience system to improve patch-level pathology reasoning by mitigating hallucinations and tool conflicts.
Discussion (0). Sign in to comment.