Learning image representations for anomaly detection: application to discovery of histological alterations in drug development

Published in

(2022)

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TL;DR

The proposed system for anomaly detection in histopathological images outperforms established AD methods on a published dataset of liver anomalies and is used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.

Authors

I. Zingman

2 papers

Fabian Heinemann

2 papers

B. Stierstorfer

1 papers

C. Lempp

1 papers

References63 items

Investigation of semi- and self-supervised learning methods in the histopathological domain.

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Anomalib: A Deep Learning Library for Anomaly Detection

Self-Supervised Representation Learning: Introduction, advances, and challenges

Learning image representations for anomaly detection: application to discovery of histological alterations in drug development

Published in

(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

I. Zingman

2 papers

Fabian Heinemann

2 papers

B. Stierstorfer

1 papers

C. Lempp

1 papers

References63 items

Investigation of semi- and self-supervised learning methods in the histopathological domain.

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Anomalib: A Deep Learning Library for Anomaly Detection

Self-Supervised Representation Learning: Introduction, advances, and challenges

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Towards Total Recall in Industrial Anomaly Detection

Unsupervised Anomaly Detection In Digital Pathology Using GANs

Student-Teacher Feature Pyramid Matching for Anomaly Detection

HistoNet: A Deep Learning-Based Model of Normal Histology

TL;DR

Authors

References63 items

Investigation of semi- and self-supervised learning methods in the histopathological domain.

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Anomalib: A Deep Learning Library for Anomaly Detection

Self-Supervised Representation Learning: Introduction, advances, and challenges

TL;DR

Authors

References63 items

Investigation of semi- and self-supervised learning methods in the histopathological domain.

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Anomalib: A Deep Learning Library for Anomaly Detection

Self-Supervised Representation Learning: Introduction, advances, and challenges

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Towards Total Recall in Industrial Anomaly Detection

Unsupervised Anomaly Detection In Digital Pathology Using GANs

Student-Teacher Feature Pyramid Matching for Anomaly Detection

HistoNet: A Deep Learning-Based Model of Normal Histology

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides

Deep Learning for Medical Anomaly Detection – A Survey

Exploring Simple Siamese Representation Learning

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

Learning and Evaluating Representations for Deep One-class Classification

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Measuring Domain Shift for Deep Learning in Histopathology

A Unifying Review of Deep and Shallow Anomaly Detection

Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations

Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders

Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection

Deep Nearest Neighbor Anomaly Detection

A Simple Framework for Contrastive Learning of Visual Representations

On Model Evaluation Under Non-constant Class Imbalance

Deep neural network models for computational histopathology: A survey

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning enables pathologist-like scoring of NASH models

Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

One-Class Convolutional Neural Network

Do Deep Generative Models Know What They Don't Know?

A brief review on multi-task learning

Extracellular matrix components indicate remodelling activity in different fibrosis stages of human non‐alcoholic fatty liver disease

Deep One-Class Classification

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Attention-based Deep Multiple Instance Learning

Learning Deep Features for One-Class Classification

Multi-task Self-Supervised Visual Learning

A Discriminative Feature Learning Approach for Deep Face Recognition

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

Densely Connected Convolutional Networks

SGDR: Stochastic Gradient Descent with Warm Restarts

Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Digital Image Processing

Dealing with the evaluation of supervised classification algorithms

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Classifying and segmenting microscopy images with deep multiple instance learning

An analysis of the attrition of drug candidates from four major pharmaceutical companies

Very Deep Convolutional Networks for Large-Scale Image Recognition

Alternatives to animal testing: A review

How to improve R&D productivity: the pharmaceutical industry's grand challenge

ImageNet: A large-scale hierarchical image database

Design and validation of a histological scoring system for nonalcoholic fatty liver disease

Image quality assessment: from error visibility to structural similarity

Estimating the Support of a High-Dimensional Distribution

AUTO-ENCODING VARIATIONAL BAYES

Visualizing Data using t-SNE

Support Vector Data Description

2022. NAFLD pathology and healthy tissue samples

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