63 citations · 93 across the 5 of their papers we have counts for
5 papers
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
Joseph Paul Cohen, Rupert Brooks, Sovann En +4
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imag…
Fine tuning U-Net for ultrasound image segmentation: which layers?
Mina Amiri, Rupert Brooks, Hassan Rivaz
Fine-tuning a network which has been trained on a large dataset is an alternative to full training in order to overcome the problem of scarce and expensive data in medical applicat…
On the limits of cross-domain generalization in automated X-ray prediction
Joseph Paul Cohen, Mohammad Hashir, Rupert Brooks +1
This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well across multiple different datasets. We present evidence that the issue of gene…
Breast lesion segmentation in ultrasound images with limited annotated data
Bahareh Behboodi, Mina Amiri, Rupert Brooks +1
Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic. US im…
DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
Jen-Tang Lu, Rupert Brooks, Stefan Hahn +9
We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in…