Refractive Surgery

Automated segmenta-on of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023

Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magnetic resonance imaging (MRI)-based anatomical 3D models. However, creating these models is often time-consuming and user dependent. We organized the Surgical Planning in Pediatric Neuroblastoma (SPPIN) challenge, to stimulate developments on this topic,...

The effects of the post-delay epochs on working memory error reduction.

Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color del...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of po...

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device ...

An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has ...

Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat

The development of powerful user representations is a key factor in the success of recommender systems (RecSys). Online platforms employ a range of ...

In defence of post-hoc explanations in medical AI

Since the early days of the Explainable AI movement, post-hoc explanations have been praised for their potential to improve user understanding, prom...

WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the...

Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models

Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situ...

Optimizing Post-Cancer Treatment Prognosis: A Study of Machine Learning and Ensemble Techniques

The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize pat...

Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models

Timely and accurate detection of hurricane debris is critical for effective disaster response and community resilience. While post-disaster aerial i...

Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing

Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical vari...

DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization

Natural disasters increasingly threaten communities worldwide, creating an urgent need for rapid, reliable building damage assessment to guide emerg...

Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design

The growing burden of myopia and retinal diseases necessitates more accessible and efficient eye screening solutions. This study presents a compact,...

Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models

Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on...

Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations

Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how...

Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI

This study addresses the challenge of predicting post-stroke rigidity by emphasizing feature interactions through graph-based explainable AI. Post-s...

SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis

Despite significant progress in intelligent fault diagnosis (IFD), the lack of interpretability remains a critical barrier to practical industrial a...

SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning

Masked video modeling, such as VideoMAE, is an effective paradigm for video self-supervised learning (SSL). However, they are primarily based on rec...

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

We explore how multimodal Large Language Models (mLLMs) can help researchers transcribe historical documents, extract relevant historical informatio...