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Found 31 papers, 3 papers with code
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
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.
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs
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.
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications.
Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data.
Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?
Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs.
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
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.
Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs
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.
Topological Deep Learning: Going Beyond Graph Data
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.
Robust Neonatal Face Detection in Real-world Clinical Settings
Current face detection algorithms are extremely generalized and can obtain decent accuracy when detecting the adult faces.
WiCV 2021: The Eighth Women In Computer Vision Workshop
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021.
Data-Centric AI Requires Rethinking Data Notion
The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages.
Multi-loss ensemble deep learning for chest X-ray classification
Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers.
Pattern Recognition in Vital Signs Using Spectrograms
The results from the proposed approach are promising with an accuracy of 91. 55% and 91. 67% in prediction and classification tasks respectively.
Trilateral Attention Network for Real-time Medical Image Segmentation
The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields.
Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings
However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors.
Simplicial Complex Representation Learning
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.
Persistent Homology and Graphs Representation Learning
This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology.
TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images
Topological Data Analysis (TDA) has emerged recently as a robust tool to extract and compare the structure of datasets.
Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment
We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration.
Algebraically-Informed Deep Networks (AIDN): A Deep Learning Approach to Represent Algebraic Structures
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.
Accounting for Affect in Pain Level Recognition
In this work, we address the importance of affect in automated pain assessment and the implications in real-world settings.
Cell Complex Neural Networks
Cell complexes are topological spaces constructed from simple blocks called cells.
Unified Representation Learning for Efficient Medical Image Analysis
We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks.
First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain
This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain.
Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound
Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased.
Multi-Channel Neural Network for Assessing Neonatal Pain from Videos
Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing.
A method to Suppress Facial Expression in Posed and Spontaneous Videos
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.
Neonatal Pain Expression Recognition Using Transfer Learning
In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning.
Machine-based Multimodal Pain Assessment Tool for Infants: A Review
In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.
