- Grand Challenge

Tripti Bameta

Tripti_ACTREC

India
ACTREC
Medical Oncology

Statistics

Member for 3 years, 5 months

Activity Overview

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.

PAIP 2019

Challenge User

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

PAIP2020

Challenge User

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

PAIP2021

Challenge User

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

TIGER

Challenge User

Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers

CoNIC 2022

Challenge User

Colon Nuclei Identification and Counting Challenge 2022

BCNB

Challenge User

Early Breast Cancer Core-Needle Biopsy WSI Dataset

PAIP 2023: TC prediction in pancreatic and colon cancer

Challenge User

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

UNICORN

Challenge User

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

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.