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Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective

Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective

Yu Cai
Hao Chen
Kwang-Ting Cheng
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Papers citing "Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective"

5 / 5 papers shown
Title
Autoencoders for Anomaly Detection are Unreliable
Autoencoders for Anomaly Detection are Unreliable
Roel Bouman
Tom Heskes
63
1
0
23 Jan 2025
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Yu Cai
Hao Chen
Xin Yang
Yu Zhou
Wuhan
25
17
0
08 Jun 2022
A Unified Model for Multi-class Anomaly Detection
A Unified Model for Multi-class Anomaly Detection
Zhiyuan You
Lei Cui
Yujun Shen
Kai Yang
Xin Lu
Yu Zheng
Xinyi Le
56
220
0
08 Jun 2022
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
Ujjwal Baid
S. Ghodasara
S. Mohan
Michel Bilello
Evan Calabrese
...
M. Weber
A. Mahajan
Bjoern Menze
Adam Flanders
Spyridon Bakas
129
629
0
05 Jul 2021
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
412
16,947
0
20 Dec 2013

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