Aditya Nori — accepted AI-conference papers

2024

RadEdit: stress-testing biomedical vision models via diffusion image editing

Fernando Pérez-García, Sam Bond-Taylor, Pedro Sanchez, Boris van Breugel, Daniel Coelho de Castro, Harshita Sharma +8

ECCV 2024poster

"Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical visi…

Cited by 9 Source ↗PDF ↗Scholar ↗

2023

Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro +10

CVPR 2023poster

Self-supervised learning in vision--language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not onl…

Cited by 139 Source ↗PDF ↗Scholar ↗

2022

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland +6

ECCV 2022poster

"Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision-language modelling compared to…

Cited by 306 Source ↗PDF ↗code ↗Scholar ↗

2022

Repairing Neural Networks by Leaving the Right Past Behind

Ryutaro Tanno, Melanie F. Pradier, Aditya Nori, Yingzhen Li

NeurIPS 2022accept

Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases. However, such data points that are responsible for a given failure mode are generally not known a priori, let alone a mechanism for repairing the f…

Cited by 36 Source ↗PDF ↗Scholar ↗

2020

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya Nori

ICML 2020poster

Machine learning models, especially deep neural networks are known to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used to train a model. Such privacy risks are exacerbated when a model is used for predictions on an unseen data…

Cited by 36 Source ↗PDF ↗code ↗Scholar ↗

2019

Adaptive Neural Trees

Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori

ICML 2019oral

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the t…

Cited by 221 Source ↗PDF ↗code ↗Scholar ↗

2018

Semi-Supervised Learning via Compact Latent Space Clustering

Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert +3

ICML 2018oral

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying s…

Cited by 109 Source ↗PDF ↗Scholar ↗

2016

Measuring Neural Net Robustness with Constraints

Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi

NeurIPS 2016poster

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics…

Cited by 554 Source ↗PDF ↗Scholar ↗