Paknezhad M
Improving transparency and representational generalizability through parallel continual learning
This paper takes a parallel learning approach in continual learning scenarios. We define parallel continual learning as learning a sequence of tasks where the data for the previous tasks, whose distribution may have shifted over time, are also available while learning new tasks.
ReadExplaining adversarial vulnerabiity with a data sparisty hypothesis
Keywords : Adversarial vulnerability, Adversarial attacks, Decision boundary, Regularization, Data Sparsity, Smooth SoftMax score surface
ReadExplaining adversarial vulnerabiity with a data sparisty hypothesis
Despite many proposed algorithms to provide robustness to deep learning (DL) models, DL models remain susceptible to adversarial attacks. We hypothesize that the adversarial vulnerability of DL models stems from two factors. The first factor is data sparsity which is that in the high dimensional input data space, there exist large regions outside the support of the data distribution. The second factor is the existence of many redundant parameters in the DL models. Owing to these factors, different models are able to come up with different decision boundaries with comparably high prediction accuracy.
ReadAtopic Dermatitis Classification Models of 3D Optoacoustic Mesoscopic Images
A comprehensive analysis using three machine-learning models for an AI-aided atopic dermatitis (AD) diagnosis and sub-classifying AD severities with 3D Raster Scanning Optoacoustic Mesoscopy (RSOM) images, extracted features from volumetric vascular structures and clinical information.
ReadModel Learning Analysis of 3D Optoacoustic Mesoscopic Images for the Classification of Atopic Dermatitis
Atopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging.
ReadRegional registration of whole slide image stacks containing major histological artifacts
Background High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration.
ReadExploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy
We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.
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