arXiv:2603.19957v2 Announce Type: replace-cross Abstract: Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters address complementary aspects of the problem:
The continuous advancements in AI, especially in vision-language models, are enabling increasingly sophisticated applications in specialized domains like medical diagnostics, pushing the boundaries of what these systems can achieve.
This development represents a significant step towards more accurate and automated diagnostic tools in pathology, potentially transforming clinical workflows and improving patient outcomes through precise, structured analysis.
The ability to generate structured pathology reports directly from raw data using AI could streamline diagnostic processes, reduce human error, and provide richer, more consistent diagnostic information compared to existing flat labels or free-form text.
- · Healthcare AI companies
- · Hospitals and diagnostic labs
- · Patients
- · Medical researchers
- · Traditional pathology report systems
- · Manual report transcription services
Improved efficiency and accuracy in pathological diagnoses due to automated structured report generation.
Accelerated development of personalized treatment plans based on more comprehensive and consistent diagnostic data.
Potential for new drug discovery and disease understanding through large-scale, structured analysis of pathology reports across diverse patient populations.
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Read at arXiv cs.AI