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20192023
most citedSelective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification

140 citations · 179 across the 13 of their papers we have counts for

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5 papers · 1 filter

cs.CV2023

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang +3

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exist…

cs.CV2023

Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays

Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang +3

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its per…

cs.CV2020★ 10 cited

Region Comparison Network for Interpretable Few-shot Image Classification

Zhiyu Xue, Lixin Duan, Wen Li +2

While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. Howeve…

cs.CV2020★ 5 cited

Synthetic Sample Selection via Reinforcement Learning

Jiarong Ye, Yuan Xue, L. Rodney Long +4

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality c…

cs.CV2019★ 4 cited

Selective Synthetic Augmentation with Quality Assurance

Yuan Xue, Jiarong Ye, Rodney Long +3

Supervised training of an automated medical image analysis system often requires a large amount of expert annotations that are hard to collect. Moreover, the proportions of data av…