Search Results for author: Raunak Dey

Found 5 papers, 4 papers with code

ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network

no code implementations16 Dec 2021 Raunak Dey, Wenbo Sun, Haibo Xu, Yi Hong

In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans.

Brain Tumor Segmentation Lesion Segmentation +3

ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation

1 code implementation5 Mar 2021 Raunak Dey, Yi Hong

We introduce a neural network framework, utilizing adversarial learning to partition an image into two cuts, with one cut falling into a reference distribution provided by the user.

Brain Tumor Segmentation Lesion Segmentation +3

Hybrid Cascaded Neural Network for Liver Lesion Segmentation

2 code implementations11 Sep 2019 Raunak Dey, Yi Hong

Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care.

Lesion Segmentation Segmentation

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