35 citations · 38 across the 14 of their papers we have counts for
14 papers
ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations
George V. Jose, Thao Liang Chiam, Toby Hughes +4
Prototype-based networks provide inherently interpretable classification by linking predictions to learned exemplars, but their use in 3D point clouds and clinical surface-pair rea…
Neuron Incidence Redistribution for Fairness in Medical Image Classification
Abin Shoby, Lyle John Palmer, Nikhil Cherian Kurian
Deep learning models for medical image classification are susceptible to subgroup performance disparities across demographic attributes such as age, gender, and race. We identify a…
Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +2
Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with disease prevalence. In practice,…
Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification
Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +6
Diagnostic performance in medical AI varies systematically across demographic groups, yet subgroup AUC can mask clinically important disparities. At a fixed inference-time operatin…
PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
Akhila Krishna, Nikhil Cherian Kurian, Abhijeet Patil +2
Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessibl…
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training
Abhijeet Patil, Harsh Diwakar, Jay Sawant +5
Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic soft…
Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis
Akhila Krishna K, Ravi Kant Gupta, Nikhil Cherian Kurian +2
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spa…
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring
Nikhil Cherian Kurian, Varsha S, Abhijit Patil +2
Semi-supervised learning (semi-SL) is a promising alternative to supervised learning for medical image analysis when obtaining good quality supervision for medical imaging is diffi…
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers
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…
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
Tirupati Saketh Chandr, Sahar Almahfouz Nasser, Nikhil Cherian Kurian +1
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysi…
EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning
Ravi Kant Gupta, Shivani Nandgaonkar, Nikhil Cherian Kurian +2
The standard diagnostic procedures for targeted therapies in lung cancer treatment involve histological subtyping and subsequent detection of key driver mutations, such as EGFR. Ev…
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation
Nikhil Cherian Kurian, Amit Lohan, Gregory Verghese +8
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work. Firstly, the accuracy of vanilla U-Ne…
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Saqib Shamsi +2
We present WSSAMNet, a weakly supervised method for medical image registration. Ours is a two step method, with the first step being the computation of segmentation masks of the fi…
Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images
Abhijeet Patil, Mohd. Talha, Aniket Bhatia +4
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different. Due to variations in staining protocols, rea…