Goal: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies-a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. Results: In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. Conclusions: We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.

Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone’s inertial sensor readings are used as input to a machine learning algorithm which performs the classification. There are several approaches to tackle such a task: feature based approaches, one dimensional deep learning algorithms, and two dimensional deep learning architectures. When using deep learning approaches, feature engineering is redundant. In addition, while utilizing two-dimensional deep learning approaches enables to utilize methods from the well-established computer vision domain. In this paper, a framework for smartphone location and human activity recognition, based on the smartphone’s inertial sensors, is proposed. The contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, and transfer learning from computer vision domain to the inertial sensors classification problem. Four different datasets are employed to show the benefits of using the proposed approach. In addition, as the proposed framework performs classification on inertial sensors readings, it can be applied for other classification tasks using inertial data. It can also be adopted to handle other types of sensory data collected for a classification task.

Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.

The rapid scaling of Genomic Foundation Models (GFMs) has created a critical need for standardized evaluation frameworks. Current benchmarking practices are often fragmented, relying on model-specific preprocessing and inconsistent metric implementations that hinder reproducible comparisons. We present GFMBench-API, a high-level Python interface designed to unify the evaluation lifecycle of GFMs. GFMBench-API provides a modular “middleware” architecture that decouples model-specific tokenization and embedding logic from task-specific data streams and performance metrics. By standardizing the input/output schemas for common genomic tasks, such as regulatory element prediction, variant effect scoring, and long-range interaction mapping, GFMBench-API enables researchers to integrate new models or tasks with minimal “glue code.” Our interface ensures mathematical consistency across evaluations, providing a robust foundation for the transparent and systematic benchmarking of GFMs.

Video retargeting for digital face animation is used in virtual reality, social media, gaming, movies, and video conference, aiming to animate avatars’ facial expressions based on videos of human faces. The standard method to represent facial expressions for 3D characters is by blendshapes, a vector of weights representing the avatar’s neutral shape and its variations under facial expressions, e.g., smile, puff, blinking. Datasets of paired frames with blendshape vectors are rare, and labeling can be laborious, time-consuming, and subjective. In this work, we developed an approach that handles the lack of appropriate datasets. Instead, we used a synthetic dataset of only one character. To generalize various characters, we re-represented each frame to face landmarks. We developed a unique deep learning architecture that groups landmarks for each facial organ and connects them to relevant blendshape weights. Additionally, we incorporated complementary methods for facial expressions that landmarks did not represent well and gave special attention to eye expressions. We have demonstrated the superiority of our approach to previous research in qualitative and quantitative metrics. Our approach achieved a higher mean opinion score (MOS) of 68% and a lower mean square error (MSE) of 44.2% when tested on videos with various users and expressions. Keywords AR/VR Deep learning 3D facial animation Computer graphics Video retargeting Motion capture

Video retargeting for digital face animation is used in virtual reality, social media, gaming, movies, and video conference, aiming to animate avatars' facial expressions based on videos of human faces. The standard method to represent facial expressions for 3D characters is by blendshapes, a vector of weights representing the avatar's neutral shape and its variations under facial expressions, e.g., smile, puff, blinking. Datasets of paired frames with blendshape vectors are rare, and labeling can be laborious, time-consuming, and subjective. In this work, we developed an approach that handles the lack of appropriate datasets. Instead, we used a synthetic dataset of only one character. To generalize various characters, we re-represented each frame to face landmarks. We developed a unique deep-learning architecture that groups landmarks for each facial organ and connects them to relevant blendshape weights. Additionally, we incorporated complementary methods for facial expressions that landmarks did not represent well and gave special attention to eye expressions. We have demonstrated the superiority of our approach to previous research in qualitative and quantitative metrics. Our approach achieved a higher MOS of 68% and a lower MSE of 44.2% when tested on videos with various users and expressions.