8 citations · 18 across the 12 of their papers we have counts for
13 papers
Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples
Samra Irshad, Seungkyu Lee, Nassir Navab +2
The presence of adversarial examples in the physical world poses significant challenges to the deployment of Deep Neural Networks in safety-critical applications such as autonomous…
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
Hao Ding, Yuqian Zhang, Tuxun Lu +39
Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptibl…
LINe: Out-of-Distribution Detection by Leveraging Important Neurons
Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim
It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-…
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability
Soyoun Won, Sung-Ho Bae, Seong Tae Kim
Mixed sample data augmentation strategies are actively used when training deep neural networks (DNNs). Recent studies suggest that they are effective at various tasks. However, the…
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning
Felix Buchert, Nassir Navab, Seong Tae Kim
The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expen…
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang +5
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced. Training…
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
Tobias Czempiel, Coco Rogers, Matthias Keicher +7
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been…
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Yang Zhang, Ashkan Khakzar, Yawei Li +3
One principal approach for illuminating a black-box neural network is feature attribution, i.e. identifying the importance of input features for the network's prediction. The predi…
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali +6
Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic…
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger +5
Convolutional neural networks are showing promise in the automatic diagnosis of thoracic pathologies on chest x-rays. Their black-box nature has sparked many recent works to explai…
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Ashkan Khakzar, Yang Zhang, Wejdene Mansour +5
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays. In order to establish trust in the clinical routine, the networks'…
Neural Response Interpretation through the Lens of Critical Pathways
Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja +3
Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequ…
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy +4
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically gene…