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∙ 04/04/2023

One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demon...

0 Chaoning Zhang, et al. ∙

∙ 03/26/2023

Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability

Data augmentation strategies are actively used when training deep neural...

0 Soyoun Won, et al. ∙

∙ 03/24/2023

LINe: Out-of-Distribution Detection by Leveraging Important Neurons

It is important to quantify the uncertainty of input samples, especially...

0 Yong Hyun Ahn, et al. ∙

∙ 10/09/2022

Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

Quantitative assessment of the abdominal region from clinically acquired...

0 Samra Irshad, et al. ∙

∙ 07/25/2022

Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning

The availability of large labeled datasets is the key component for the ...

0 Felix Buchert, et al. ∙

∙ 03/21/2022

Longitudinal Self-Supervision for COVID-19 Pathology Quantification

Quantifying COVID-19 infection over time is an important task to manage ...

6 Tobias Czempiel, et al. ∙

∙ 10/04/2021

Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information

One principal approach for illuminating a black-box neural network is fe...

4 Yang Zhang, et al. ∙

∙ 10/03/2021

Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans

Consistent segmentation of COVID-19 patient's CT scans across multiple t...

0 Michelle Xiao-Lin Foo, et al. ∙

∙ 04/04/2021

Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models

Convolutional neural networks are showing promise in the automatic diagn...

0 Ashkan Khakzar, et al. ∙

∙ 04/01/2021

Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features

Neural networks have demonstrated remarkable performance in classificati...

0 Ashkan Khakzar, et al. ∙

∙ 03/31/2021

Neural Response Interpretation through the Lens of Critical Pathways

Is critical input information encoded in specific sparse pathways within...

12 Ashkan Khakzar, et al. ∙

∙ 03/19/2021

GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images

Disentangled representations can be useful in many downstream tasks, hel...

26 Aadhithya Sankar, et al. ∙

∙ 03/12/2021

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

Chest computed tomography (CT) has played an essential diagnostic role i...

14 Seong Tae Kim, et al. ∙

∙ 03/05/2021

OperA: Attention-Regularized Transformers for Surgical Phase Recognition

In this paper we introduce OperA, a transformer-based model that accurat...

10 Tobias Czempiel, et al. ∙

∙ 11/10/2020

Self-Supervised Out-of-Distribution Detection in Brain CT Scans

Medical imaging data suffers from the limited availability of annotation...

0 Abinav Ravi Venkatakrishnan, et al. ∙

∙ 04/07/2020

Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR...

9 Stefan Denner, et al. ∙

∙ 04/05/2020

Confident Coreset for Active Learning in Medical Image Analysis

Recent advances in deep learning have resulted in great successes in var...

0 Seong Tae Kim, et al. ∙

∙ 02/26/2020

Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"

Spine injections are commonly performed in several clinical procedures. ...

9 Maria Tirindelli, et al. ∙

∙ 11/25/2019

Explaining Neural Networks via Perturbing Important Learned Features

Attributing the output of a neural network to the contribution of given ...

54 Ashkan Khakzar, et al. ∙

∙ 06/10/2019

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

The ambiguity of the decision-making process has been pointed out as the...

0 Hyebin Lee, et al. ∙

∙ 09/17/2018

Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation

This paper deals with a method for generating realistic labeled masses. ...

0 Jae-Hyeok Lee, et al. ∙

∙ 05/23/2018

ICADx: Interpretable computer aided diagnosis of breast masses

In this study, a novel computer aided diagnosis (CADx) framework is devi...

0 Seong Tae Kim, et al. ∙

∙ 11/29/2017

Interpretable Facial Relational Network Using Relational Importance

Human face analysis is an important task in computer vision. According t...

0 Seong Tae Kim, et al. ∙

∙ 11/28/2017

Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data

In face-related applications with a public available dataset, synthesizi...

0 Geonmo Gu, et al. ∙

∙ 07/31/2017

Convolution with Logarithmic Filter Groups for Efficient Shallow CNN

In convolutional neural networks (CNNs), the filter grouping in convolut...

0 Tae Kwan Lee, et al. ∙

∙ 05/31/2017

EvaluationNet: Can Human Skill be Evaluated by Deep Networks?

With the recent substantial growth of media such as YouTube, a considera...

0 Seong Tae Kim, et al. ∙

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