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Found 14 papers, 4 papers with code
Emulators for stellar profiles in binary population modeling
Knowledge about the internal physical structure of stars is crucial to understanding their evolution.
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
Radiomics Boosts Deep Learning Model for IPMN Classification
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation
In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training.
Domain Generalization with Correlated Style Uncertainty
In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
TinyVIRAT: Low-resolution Video Action Recognition
In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions.
Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos
For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets.
Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term.
Patch-Based Image Inpainting with Generative Adversarial Networks
Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks.
Deep Stacked Networks with Residual Polishing for Image Inpainting
Then the second network modifies the repaired image to cancel the noise introduced by the first network.
