Search Results for author: Jeremy Tan

Found 12 papers, 6 papers with code

Detecting Outliers with Poisson Image Interpolation

1 code implementation6 Jul 2021 Jeremy Tan, Benjamin Hou, Thomas Day, John Simpson, Daniel Rueckert, Bernhard Kainz

We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.

Anomaly Detection Image Reconstruction

Detecting Outliers with Foreign Patch Interpolation

1 code implementation9 Nov 2020 Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz

A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.

Ultrasound Video Summarization using Deep Reinforcement Learning

1 code implementation19 May 2020 Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, Daniel Rueckert, Bernhard Kainz

We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.

reinforcement-learning Video Summarization

Semi-supervised Learning of Fetal Anatomy from Ultrasound

no code implementations30 Aug 2019 Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz

Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images.

General Classification

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

1 code implementation ICLR 2019 Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.

Domain Adaptation Outlier Detection +1

Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging

no code implementations28 May 2018 Ahmet Tuysuzoglu, Jeremy Tan, Kareem Eissa, Atilla P. Kiraly, Mamadou Diallo, Ali Kamen

We have trained this network using ~4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations.

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