| Title: |
Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology |
| Authors: |
Verma, Ruchika; Kandoi, Shrishtee; Afzal, Robina; Chen, Shengjia; Jegminat, Jannes; Karlovich, Michael W.; Umphlett, Melissa; Richardson, Timothy E.; Clare, Kevin; Hossain, Quazi; Samanamud, Jorge; Faust, Phyllis L.; Louis, Elan D.; McKee, Ann C.; Stein, Thor D.; Cherry, Jonathan D.; Mez, Jesse; McGoldrick, Anya C.; Mora, Dalilah D. Quintana; Nirenberg, Melissa J.; Walker, Ruth H.; Mendez, Yolfrankcis; Morgello, Susan; Dickson, Dennis W.; Murray, Melissa E.; Cordon-Cardo, Carlos; Tsankova, Nadejda M.; Walker, Jamie M.; Dangoor, Diana K.; McQuillan, Stephanie; Thorn, Emma L.; De Sanctis, Claudia; Li, Shuying; Fuchs, Thomas J.; Farrell, Kurt; Crary, John F.; Campanella, Gabriele |
| Publication Year: |
2025 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Computer Vision and Pattern Recognition; Artificial Intelligence |
| Description: |
Foundation models have transformed computational pathology by providing generalizable representations from large-scale histology datasets. However, existing models are predominantly trained on surgical pathology data, which is enriched for non-nervous tissue and overrepresents neoplastic, inflammatory, metabolic, and other non-neurological diseases. Neuropathology represents a markedly different domain of histopathology, characterized by unique cell types (neurons, glia, etc.), distinct cytoarchitecture, and disease-specific pathological features including neurofibrillary tangles, amyloid plaques, Lewy bodies, and pattern-specific neurodegeneration. This domain mismatch may limit the ability of general-purpose foundation models to capture the morphological patterns critical for interpreting neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and cerebellar ataxias. To address this gap, we developed NeuroFM, a foundation model trained specifically on whole-slide images of brain tissue spanning diverse neurodegenerative pathologies. NeuroFM demonstrates superior performance compared to general-purpose models across multiple neuropathology-specific downstream tasks, including mixed dementia disease classification, hippocampal region segmentation, and neurodegenerative ataxia identification encompassing cerebellar essential tremor and spinocerebellar ataxia subtypes. This work establishes that domain-specialized foundation models trained on brain tissue can better capture neuropathology-specific features than models trained on general surgical pathology datasets. By tailoring foundation models to the unique morphological landscape of neurodegenerative diseases, NeuroFM enables more accurate and reliable AI-based analysis for brain disease diagnosis and research, setting a precedent for domain-specific model development in specialized areas of digital pathology. |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2512.05993 |
| Availability: |
http://arxiv.org/abs/2512.05993 |
| Accession Number: |
edsbas.B622784C |
| Database: |
BASE |