Although concept-based approaches are widely used to explain a model's behavior and assess the contributions of different concepts in decision-making, identifying relevant concepts can be challenging for non-experts. This paper introduces a novel method that simplifies concept selection by leveraging the capabilities of a state-of-the-art large Vision-Language Model (VLM). Our method employs a VLM to select textual concepts that describe the classes in the target dataset. We then transform these influential textual concepts into human-readable image concepts using a text-to-image model. This process allows us to explain the targeted network in a post-hoc manner. Further, we use directional derivatives and concept activation vectors to quantify the importance of the generated concepts. We evaluate our method on a neonatal pain classification task, analyzing the sensitivity of the model's output for the generated concepts. The results demonstrate that the VLM not only generates coherent and meaningful concepts that are easily understandable by non-experts but also achieves performance comparable to that of natural image concepts without the need for additional annotation costs.

Background: The need for sharing large-scale datasets in training deep learning models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.Method: In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized a publicly available facial database and focused on evaluating the trade-offs inherent in image encoding techniques, with a special emphasis on balancing privacy and model accuracy.Conclusion: This study navigate the balance between protecting sensitive data and meeting the data demands necessary for effective model training. It sheds light on the intricate trade-offs among different image encoding techniques and offers insights into finding an optimal balance between privacy protection and model performance.

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

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. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://pyt-team.github.io.

Deep Learning (DL)-based solutions have shown promising performance in assessing neonatal pain. However, the occlusion of the visual modality (face and body) is common in clinical settings due to several factors, including a prone sleeping position, low light, or swaddling. In such scenarios, other pain signals, such as audio signals, can be used as the major behavioral signs of pain. Although DL-based methods are proposed to assess pain from audio, these methods lack transparency and explainability (black box), which can decrease the user's trust in the automated decision. In this work, we visualize the neonate's audio signal as a spectrogram image to classify it as pain or no pain and present an instance-based approach for explaining the decision of the black-box model. Further, this work provides an analysis of the most helpful and harmful training instances using an influence score followed by assessing their impact on pain prediction. Experimental results demonstrate that the proposed approach can detect and remove harmful instances, eventually leading to a compressed dataset. Our results also show that the proposed work can add explainability to the current DL-based pain detection methods, which can enhance users' trust and provide a viable approach toward pain assessment in clinical settings.

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. 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. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (p < 0.05) in cross-domain generalization through our approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.

Despite the promising performance of automated pain assessment methods, current methods suffer from performance generalization due to the lack of relatively large, diverse, and annotated pain datasets. Further, the majority of current methods do not allow responsible interaction between the model and user, and do not take different internal and external factors into consideration during the model's design and development. This article aims to provide an efficient cooperative learning framework for the lack of annotated data while facilitating responsible user communication and taking individual differences into consideration during the development of pain assessment models. Our results using body and muscle movement data, collected from wearable devices, demonstrate that the proposed framework is effective in leveraging both the human and the machine to efficiently learn and predict pain.

Deep learning has a huge potential to transform echocardiography in clinical practice and point of care ultrasound testing by providing real-time analysis of cardiac structure and function. Automated echocardiography analysis is benefited through use of machine learning for tasks such as image quality assessment, view classification, cardiac region segmentation, and quantification of diagnostic indices. By taking advantage of high-performing deep neural networks, we propose a novel and eicient real-time system for echocardiography analysis and quantification. Our system uses a self-supervised modality-specific representation trained using a publicly available large-scale dataset. The trained representation is used to enhance the learning of target echo tasks with relatively small datasets. We also present a novel Trilateral Attention Network (TaNet) for real-time cardiac region segmentation. The proposed network uses a module for region localization and three lightweight pathways for encoding rich low-level, textural, and high-level features. Feature embeddings from these individual pathways are then aggregated for cardiac region segmentation. This network is fine-tuned using a joint loss function and training strategy. We extensively evaluate the proposed system and its components, which are echo view retrieval, cardiac segmentation, and quantification, using four echocardiography datasets. Our experimental results show a consistent improvement in the performance of echocardiography analysis tasks with enhanced computational eiciency that charts a path toward its adoption in clinical practice. Specifically, our results show superior real-time performance in retrieving good quality echo from individual cardiac view, segmenting cardiac chambers with complex overlaps, and extracting cardiac indices that highly agree with the experts' values. The source code of our implementation can be found in the project's GitHub page.

Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several painful procedures during their hospitalization. Assessing neonatal pain is difficult because the current standard for assessment is subjective, inconsistent, and discontinuous. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term outcomes. In this paper, we present a comprehensive pain assessment system that utilizes facial expressions along with crying sounds, body movement, and vital sign changes. The proposed automatic system generates a standardized pain assessment comparable to those obtained by conventional nurse-derived pain scores. The system achieved 95.56 percent accuracy using decision fusion of different pain responses that were recorded in a challenging clinical environment. In addition to the decision fusion, we present the performance of multimodal assessment using other fusion schemes as well as a unimodal assessment approach. We also discuss the impact of different factors (e.g., gestational age) on pain, propose several group-specific models for pain assessment (e.g., pre-term and full-term models), and compare the performance of these models with the performance of general models. While further research is needed, our results show that the automatic assessment of neonatal pain is a viable and more efficient alternative to the manual assessment.

Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are time-series signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55% and 91.67% in prediction and classification tasks respectively.

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent Neural Network (RNN). Although acute and postoperative pain have some common characteristics (e.g., visual action units), postoperative pain has a different dynamic, and it evolves in a unique pattern over time. Our experimental results indicate a clear difference between the pattern of acute and postoperative pain. They also suggest the efficiency of using a combination of bilinear CNN with RNN model for the continuous assessment of postoperative pain intensity.

Doppler Echocardiography is critical for measuring abnormal cardiac function and diagnosing valvular stenosis and regurgitation. The current practice for assessing and interpreting Doppler echo images is time-consuming and depends highly on the experience of the operator. The limitations of this practice can be mitigated using fully automated intelligent systems. Essential first steps toward comprehensive computer-assisted Doppler echocardiographic interpretation include automatic classification into view/flow categories and goodness assessment of these flows. In this paper, we propose a deep learning-based method for Doppler flow classification and goodness assessment. The method has been trained on labeled images representing a wide range of real-world clinical variation. Our method, when evaluated on unseen data, achieved overall accuracies of 91.6% and 88.9% for flow classification and goodness assessment, respectively. While further research is needed, these results are encouraging and prove the feasibility of using fully automated intelligent systems for analyzing and interpreting Doppler echo images.

The current standard for assessing neonatal pain is discontinuous and suffers from inter-observer variations, which can result in delayed intervention and inconsistent treatment of pain. Therefore, it is critical to address the shortcomings of the current standard and develop continuous and less subjective pain assessment tools. Convolutional Neural Networks have gained much popularity in the last decades due to the wide range of its successful applications in medical image analysis, object recognition, and emotion recognition. In this paper, we propose a Neonatal Convolutional Neural Network, designed and trained end-to-end to detect neonatal pain. We evaluated the proposed network in two data sets of neonates and compared its performance to the performance of ResNet architecture in the same data sets. Our proposed method outperformed ResNet in recognizing neonates' pain and achieved around 91.00% accuracy. While further research is needed, our preliminary results suggest that the presented network can be used for automatic pain assessment, and possibly similar applications. It also suggests that the automatic recognition of neonatal pain provides a viable and more efficient alternative to the current standard of pain assessment.

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.

Current practices of assessing infants' pain depends on the observer's subjective and potentially inconsistent judgment and requires continuous monitoring by care providers. Therefore, pain may be misinterpreted or totally missed leading to misdiagnosis and over/under treatment. To address these shortcomings, current practices can be augmented with a machine-based assessment system that monitors various pain cues and provides an objective and continuous assessment of pain. Although several machine-based pain assessment approaches have been introduced, the majority of these approaches assess pain based on analysis of a single pain indicator (i.e., unimodal). In this paper, we propose an automated multimodal approach that utilizes a combination of both behavioral and physiological pain indicators to assess infants' pain. We also present a unimodal approach that depends on a single pain indicator for assessment. Recogsnizing pain using a single indicator yielded 88%, 85%, and 82% overall accuracies for facial expression, body movement, and vital signs, respectively. Combining facial expression, body movement, and changes in vital signs (i.e., the multimodal approach) for assessment achieved 95% overall accuracy. These preliminarily results indicate that utilizing both behavioral and physiological pain indicators could provide a better and more reliable assessment of infants' pain.