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Found 20 papers, 7 papers with code
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications.
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine.
The Clever Hans Effect in Unsupervised Learning
Unsupervised learning has become an essential building block of AI systems.
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95. 0% (stomach) and 91. 0% (colon) AUROC and generalized across scanners and hospitals.
RudolfV: A Foundation Model by Pathologists for Pathologists
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research.
Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides
Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model.
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.
Ranked #1 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
Hierarchical Multimodal Variational Autoencoders
Humans find structure in natural phenomena by absorbing stimuli from multiple input sources such as vision, text, and speech.
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
Geometric Disentanglement by Random Convex Polytopes
We propose a new geometric method for measuring the quality of representations obtained from deep learning.
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Ranked #5 on
Anomaly Detection
on One-class ImageNet-30
(using extra training data)
The Clever Hans Effect in Anomaly Detection
The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features.
Anomaly Detection
Explainable Artificial Intelligence (XAI)
+1
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
From Clustering to Cluster Explanations via Neural Networks
Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features.
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Deep One-Class Classification
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #34 on
Anomaly Detection
on One-class CIFAR-10
Anomaly Detection with Generative Adversarial Networks
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.
