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Gunjan Deotale

gdeotale123

  •  India
  •  Aira matrix
  •  Histopathology
Statistics
  • Member for 7 months, 3 weeks
  • 56 challenge submissions
  • 49 algorithms run

Activity Overview

BCSegmentation Logo
Breast Cancer Segmentation
Challenge User

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

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WSSS4LUAD
Challenge User

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

CoNIC-Challenge Logo
CoNIC 2022
Challenge User

Colon Nuclei Identification and Counting Challenge 2022

DRAGON Logo
Diagnostic Report Analysis: General Optimization of NLP
Challenge User

MONKEY Logo
MONKEY challenge: Detection of inflammation in kidney biopsies
Challenge User

MONKEY (Machine-learning for Optimal detection of iNflammatory cells in KidnEY)

PUMA Logo
PUMA: Panoptic segmentation of nUclei and tissue in MelanomA
Challenge User

The PUMA Challenge aims to enhance nuclei and tissue segmentation in melanoma histopathology, addressing the need for better prognostic biomarkers to predict treatment responses. Melanoma, a highly aggressive skin cancer, often requires immune checkpoint inhibition therapy, but only half of patients respond. Prognostic biomarkers like tumor infiltrating lymphocytes (TILs) correlate with better therapy responses and lower recurrence rate, but manual TIL scoring is subjective and inconsistent. Current deep learning methods underperform. The PUMA dataset includes annotated primary and metastatic melanoma regions to improve segmentation techniques. The challenge includes two tracks with tasks focused on tissue and nuclei segmentation, encouraging advanced methods to improve predictive accuracy.

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The LUNA25 Challenge
Challenge User

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Fuse My Cells: From Single View to Fused Multiview Lightsheet Im
Challenge User