Pathology Projects
Please visit the related project pages
Breast Cancer StromaSeg Glioma NucleiSeg SpaTra Ulcer EsoCount UVLMBI MELLMA PathAIData
--- Coming Soon ---
Cytology GBMAnalysis Liver Fibrosis LungMAP CRC Ki67 OMP GlandSeg CeliacTransNet MBTransNet
Data: Stanford C-path TMA, USA, ICGC/BASIS, EU
Lung
Data: LungMAP, CCHMC, USA, LC25000
Ulcer/IBD
Data: PROTECT, Dhaliwal Lab, GI, CCHMC, USA
Oral
Data: ADC, India
Colorectal Cancer (CRC)
Data: TCGA, USA
AI for Pathology
AI is becoming commonplace and in the last few years with foundation models (FMs), large language models (LLMs), agentic AI, and vision-language models (VLMs) are increasingly used in processing various pathology data (WSIs, different stains, TMAs, pathology reports,...). Prasath Lab is interested in leveraging AI/ML/DL to solve challenges in large-scale image processing in the computational pathology domain. Given the expertise and experience with the multidisciplinary projects and the proven track-record in bringing quantitative approaches from mathematics, computer science, and statistics we are well-poised to be a connector among different domains. Prasath Lab is interested in harnessing LLMs, VLMs, and FMs for extracting valuable insights from imaging and text data in the pathology. We work on adaption of digital tools in the pathology domain including but not limited to metadata streamlining, global standards, and regulatory frameworks.
We are also part of multiple collaborative efforts and our research interests span the full spectrum of clinical, and basic research pathology informatics:
AI/ML for Tissue Analytics
Generative AI for Pathology
Interpretable AI Tools
Multimodal Fusion (text, imaging, genomics,...)
Foundation Models (FMs)
Multimodal Imaging (H & E, IHC, spatial transcriptomics,...)
LLMs for Pathology
Agentic AI, Multi-agents, LLM-As-A-Judge, LLM Arbitration
Selected publications:
A. Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network. Biomedical Engineering Letters, 8(3), 321-327, August 2018. doi:10.1007/s13534-018-0077-0
T. Hayakawa, V. B. S. Prasath, H. Kawanaka, B. J. Aronow, S. Tsuruoka. Computational nuclei segmentation methods in digital pathology - A survey. Archives of Computational Methods in Engineering, 28(1), 1-13, January 2021. doi:10.1007/s11831-019-09366-4
R. Nakagaki, S. S. Debsarkar, H. Kawanaka, B. Aronow, V. B. S. Prasath. Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data. Computers in Biology and Medicine, 179, 108902, September 2024. doi:10.1016/j.compbiomed.2024.108902
S. S. Debsarkar, B. J. Aronow, V. B. S. Prasath. Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review. Computers in Biology and Medicine, 190, 110072, May 2025. doi:10.1016/j.compbiomed.2025.110072
S. Shirae, S. S. Debsarkar, H. Kawanaka, B. J. Aronow, V. B. S. Prasath. Multimodal ensemble fusion deep learning using histopathological images and clinical data for glioma subtype classification. IEEE Access, 13, 1-20, April 2025. doi:10.1109/ACCESS.2025.3556713
S. S. Debsarkar, V. B. S. Prasath. A multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology prediction. Computerized Medical Imaging and Graphics, 128, 102704, February 2026. doi:10.1016/j.compmedimag.2026.102704
S. S. Debsarkar, B. J. Aronow, V. B. S. Prasath. Unsupervised biomarker discovery leveraging foundation models: A multimodal approach to clinical data integration. Neural Computing and Applications, 38, 93, 24pp, March 2026. doi:10.1007/s00521-025-11751-z
S. Shirae, S. S. Debsarkar, H. Kawanaka, B. Aronow, V. B. S. Prasath. End-to-end multimodal multiple instance learning for cancer histopathology classification with cross-attention fusion. Journal of Medical Systems, 2026. doi:TBA
A. R. Chinnachinnanagari, S. S. Debsarkar, V. B. S. Prasath. Pathology public datasets for artificial intelligence: A systematic review. Journal of Imaging Informatics in Medicine, 2026. doi:10.1007/s10278-026-01899-y
V. B. S. Prasath. The digital transformation of anatomical pathology: A comprehensive commentary on the guidelines in the context of global standards and regulatory frameworks. Submitted, 2026.
