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Aditya Vartak

aditya.vartak

  •  India
  •  AIRA Matrix
  •  Applied Research
Statistics
  • Member for 1 year, 1 month
  • 64 challenge submissions
  • 11 algorithms run

Activity Overview

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ANHIR
Challenge User

The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.

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Breast Cancer Immunohistochemical Image Generation Challenge
Challenge User

The Breast Cancer Immunohistochemical Image Generation Challenge aims to directly generate IHC-stained breast cancer histopathology images from HE-stained images.

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The LEOPARD Challenge
Challenge User

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UNICORN
Challenge User

Grand challenge on benchmarking vision-language foundation models in the digital pathology and radiology domain

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REport Generation in pathology using Pan-Asia Giga-pixel WSIs
Challenge User

This project focuses on advancing automated pathology report generation using vision-language foundation models. It addresses the limitations of traditional NLP metrics (e.g., BLEU, METEOR, ROUGE) by emphasizing clinically relevant evaluation. The initiative includes standardized datasets, expert comparisons, and medical-domain-specific metrics to assess model performance. It also explores the integration of generated reports into diagnostic workflows with clinical feedback. To support fairness and generalizability, the challenge dataset comprises ~20,500 cases from six medical centers in Korea, Japan, India, Turkey, and Germany, promoting multicultural and multiethnic medical AI development.