58 citations · 76 across the 13 of their papers we have counts for
15 papers
Debiasing Counterfactuals In the Presence of Spurious Correlations
Amar Kumar, Nima Fathi, Raghav Mehta +4
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i.e. confounders), should they b…
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
Changjian Shui, Justin Szeto, Raghav Mehta +2
Trustworthy deployment of deep learning medical imaging models into real-world clinical practice requires that they be calibrated. However, models that are well calibrated overall…
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta +3
Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning fr…
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Raghav Mehta, Changjian Shui, Tal Arbel
Although deep learning (DL) models have shown great success in many medical image analysis tasks, deployment of the resulting models into real clinical contexts requires: (1) that…
You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
Raghav Mehta, Vítor Albiero, Li Chen +4
Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts. For example, when predicting whether an object is a cow, a model…
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +5
Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real…
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta, Changjian Shui, Brennan Nichyporuk +1
Large, annotated datasets are not widely available in medical image analysis due to the prohibitive time, costs, and challenges associated with labelling large datasets. Unlabelled…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Sub-cortical structure segmentation database for young population
Jayanthi Sivaswamy, Alphin J Thottupattu, Mythri V +3
Segmentation of sub-cortical structures from MRI scans is of interest in many neurological diagnosis. Since this is a laborious task machine learning and specifically deep learning…
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +4
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient c…
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand +3
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, accurate segmentation requires the inclusi…
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta, Angelos Filos, Yarin Gal +1
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertai…
Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays
Ghasem Pasandi, Raghav Mehta, Massoud Pedram +1
Memory accounts for a considerable portion of the total power budget and area of digital systems. Furthermore, it is typically the performance bottleneck of the processing units. T…
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
Raghav Mehta, Tal Arbel
Accurate synthesis of a full 3D MR image containing tumours from available MRI (e.g. to replace an image that is currently unavailable or corrupted) would provide a clinician as we…
To Learn or Not to Learn Features for Deformable Registration?
Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy
Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how…