no code implementations • 14 Feb 2024 • Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
no code implementations • 12 Feb 2024 • Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan, Paul H. Yi, Berkman Sahiner, Jana G. Delfino
Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model.
1 code implementation • 4 Feb 2024 • Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
no code implementations • 15 Dec 2023 • Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub
In this context, cell complexes are often seen as a subclass of hypergraphs with additional algebraic structure that can be exploited, e. g., to develop a spectral theory.
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 • 13 Jun 2022 • Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi, Peng Guo, Zhiyun Xue, Sameer K Antani
We observed that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0. 5743, 95% confidence interval: (0. 4055, 0. 7431)) compared to the individual constituent models and other ensemble methods.
3 code implementations • 1 Jun 2022 • Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations.
no code implementations • 1 Apr 2022 • Jacqueline Hausmann, Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Yu Sun
Current face detection algorithms are extremely generalized and can obtain decent accuracy when detecting the adult faces.
no code implementations • 11 Mar 2022 • Arushi Goel, Niveditha Kalavakonda, Nour Karessli, Tejaswi Kasarla, Kathryn Leonard, Boyi Li, Nermin Samet and, Ghada Zamzmi
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021.
no code implementations • 6 Oct 2021 • Mustafa Hajij, Ghada Zamzmi, Karthikeyan Natesan Ramamurthy, Aldo Guzman Saenz
The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages.
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 • 5 Aug 2021 • Sidharth Srivatsav Sribhashyam, Md Sirajus Salekin, Dmitry Goldgof, Ghada Zamzmi, Mark Last, Yu Sun
The results from the proposed approach are promising with an accuracy of 91. 55% and 91. 67% in prediction and classification tasks respectively.
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 • 6 Mar 2021 • Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Vasileios Maroulas, Xuanting Cai
In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved.
no code implementations • 25 Feb 2021 • Mustafa Hajij, Ghada Zamzmi, Xuanting Cai
This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology.
no code implementations • 21 Jan 2021 • Mustafa Hajij, Ghada Zamzmi, Fawwaz Batayneh
Topological Data Analysis (TDA) has emerged recently as a robust tool to extract and compare the structure of datasets.
no code implementations • 3 Dec 2020 • Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun
We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration.
1 code implementation • 2 Dec 2020 • Mustafa Hajij, Ghada Zamzmi, Matthew Dawson, Greg Muller
The deep networks obtained via \textbf{AIDN} are \textit{algebraically-informed} in the sense that they satisfy the algebraic relations of the presentation of the algebraic structure that serves as the input to the algorithm.
no code implementations • 15 Nov 2020 • Md Taufeeq Uddin, Shaun Canavan, Ghada Zamzmi
In this work, we address the importance of affect in automated pain assessment and the implications in real-world settings.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Mustafa Hajij, Kyle Istvan, Ghada Zamzmi
Cell complexes are topological spaces constructed from simple blocks called cells.
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 • 24 Mar 2020 • Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun
This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain.
no code implementations • 5 Sep 2019 • Md Sirajus Salekin, Ghada Zamzmi, Rahul Paul, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun
Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased.
no code implementations • 25 Aug 2019 • Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun
Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing.
no code implementations • 4 Oct 2018 • Ghada Zamzmi, Gabriel Ruiz, Matthew Shreve, Dmitry Goldgof, Rangachar Kasturi, Sudeep Sarkar
We address the problem of suppressing facial expressions in videos because expressions can hinder the retrieval of important information in applications such as face recognition.
no code implementations • 4 Jul 2018 • Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun
In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning.
no code implementations • 1 Jul 2016 • Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun, Terri Ashmeade
In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.