Search Results for author: Nikhil Cherian Kurian

Found 8 papers, 1 papers with code

Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images

no code implementations30 Nov 2020 Abhijeet Patil, Mohd. Talha, Aniket Bhatia, Nikhil Cherian Kurian, Sammed Mangale, Sunil Patel, Amit Sethi

Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different.

Color Normalization

EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning

no code implementations26 Aug 2022 Ravi Kant Gupta, Shivani Nandgaonkar, Nikhil Cherian Kurian, Swapnil Rane, Amit Sethi

With our pipeline, we achieved an average area under the curve (AUC) of 0. 964 for tumor detection, and 0. 942 for histological subtyping between adenocarcinoma and squamous cell carcinoma on the TCGA dataset.

whole slide images

Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer

1 code implementation15 Sep 2022 Tirupati Saketh Chandr, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi

The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.

Mitosis Detection

Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers

no code implementations22 Feb 2023 Pranav Jeevan, Nikhil Cherian Kurian, Amit Sethi

Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.

Breast Cancer Histology Image Classification Image Classification

Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring

no code implementations17 Mar 2023 Nikhil Cherian Kurian, Varsha S, Abhijit PATIL, Shashikant Khade, Amit Sethi

The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework.

Self-Supervised Learning whole slide images

Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis

no code implementations16 Jul 2023 Akhila Krishna K, Ravi Kant Gupta, Nikhil Cherian Kurian, Pranav Jeevan, Amit Sethi

The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection.

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