no code implementations • 20 Sep 2023 • Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhaohui Liang, Zhiyun Xue, Sameer Antani
Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications.
no code implementations • 18 Sep 2023 • Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhaohui Liang, Zhiyun Xue, Sameer Antani
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data.
no code implementations • 10 Jan 2023 • Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi, Zhiyun Xue, Sameer Antani
Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs.
no code implementations • 4 Nov 2022 • Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi, Zhiyun Xue, Sameer Antani
In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles.
no code implementations • 19 Feb 2022 • Harshit Parmar, Brian Nutter, Rodney Long, Sameer Antani, Sunanda Mitra
Conclusion: The multistage k-means approach can provide functional parcellations of the brain using resting state fMRI data.
no code implementations • 18 Jan 2022 • Anabik Pal, Zhiyun Xue, Sameer Antani
We believe that the present research shows a novel direction in developing criteria-specific custom deep models for cervical image classification by combining images from different sources unlabeled and/or labeled with varying criteria, and addressing image access restrictions.
no code implementations • 10 Nov 2021 • Yuan Xue, Jiarong Ye, Qianying Zhou, Rodney Long, Sameer Antani, Zhiyun Xue, Carl Cornwell, Richard Zaino, Keith Cheng, Xiaolei Huang
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
no code implementations • 5 Nov 2021 • Sivaramakrishnan Rajaraman, Gregg Cohen, Lillian Spear, Les Folio, Sameer Antani
It is observed that the bone suppression model ensemble outperformed the individual models in terms of MS-SSIM and other metrics.
no code implementations • 29 Sep 2021 • Sivaramakrishnan Rajaraman, Ghada Zamzmi, Sameer Antani
Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers.
no code implementations • 29 Sep 2021 • Sivaramakrishnan Rajaraman, Prasanth Ganesan, Sameer Antani
In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones.
no code implementations • 17 Jun 2021 • Ghada Zamzmi, Vandana Sachdev, Sameer Antani
The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields.
no code implementations • 9 Apr 2021 • Sivaramakrishnan Rajaraman, Ghada Zamzmi, Les Folio, Philip Alderson, Sameer Antani
However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors.
no code implementations • 21 Feb 2021 • Sivaramakrishnan Rajaraman, Les Folio, Jane Dimperio, Philip Alderson, Sameer Antani
We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution as well as from cross-institutional collections (p < 0. 05).
no code implementations • 26 Aug 2020 • Jiarong Ye, Yuan Xue, L. Rodney Long, Sameer Antani, Zhiyun Xue, Keith Cheng, Xiaolei Huang
However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.
1 code implementation • 22 Jul 2020 • Sudhir Sornapudi, R. Joe Stanley, William V. Stoecker, Rodney Long, Zhiyun Xue, Rosemary Zuna, Shelliane R. Frazier, Sameer Antani
Cervical cancer is one of the deadliest cancers affecting women globally.
no code implementations • 19 Jun 2020 • Ghada Zamzmi, Sivaramakrishnan Rajaraman, Sameer Antani
We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks.
no code implementations • 9 Dec 2019 • Yuan Xue, Jiarong Ye, Rodney Long, Sameer Antani, Zhiyun Xue, Xiaolei Huang
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
no code implementations • 2 Oct 2019 • Sudhir Sornapudi, G. T. Brown, Zhiyun Xue, Rodney Long, Lisa Allen, Sameer Antani
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data.
no code implementations • 24 Jul 2019 • Yuan Xue, Qianying Zhou, Jiarong Ye, L. Rodney Long, Sameer Antani, Carl Cornwell, Zhiyun Xue, Xiaolei Huang
Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations.