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20132023
most citedA Novel Collaborative Self-Supervised Learning Method for Radiomic Data

15 citations · 35 across the 10 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

eess.IV2023★ 9 cited

Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment

Zhiyuan Li, Hailong Li, Anca L. Ralescu +5

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising ou…

cs.CV2023★ 2 cited

On the ability of CNNs to extract color invariant intensity based features for image classification

Pradyumna Elavarthi, James Lee, Anca Ralescu

Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training di…

eess.IV2023

Semantic Segmentation of Porosity in 4D Spatio-Temporal X-ray μCT of Titanium Coated Ni wires using Deep Learning

Pradyumna Elavarthi, Arun Bhattacharjee, Ashley Paz y Puente +1

A fully convolutional neural network was used to measure the evolution of the volume fraction of two different Kirkendall pores during the homogenization of Ti coated Ni wires. Tra…

cs.CV2023★ 1 cited

Learning Empirical Bregman Divergence for Uncertain Distance Representation

Zhiyuan Li, Ziru Liu, Anna Zou +1

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep netw…

eess.IV2023★ 15 cited

A Novel Collaborative Self-Supervised Learning Method for Radiomic Data

Zhiyuan Li, Hailong Li, Anca L. Ralescu +3

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, wh…

cs.CV2023★ 3 cited

Learning Generalized Hybrid Proximity Representation for Image Recognition

Zhiyuan Li, Anca Ralescu

Recently, deep metric learning techniques received attention, as the learned distance representations are useful to capture the similarity relationship among samples and further im…