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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 patient care and recovery. This initiative seeks to improve the effectiveness of cancer treatment, emphasizing the significance of each patient's journey and well-being. Our focus is to provide patients with valuable insight into their treatment timelin...

Testing LLMs' Capabilities in Annotating Translations Based on an Error Typology Designed for LSP Translation: First Experiments with ChatGPT

This study investigates the capabilities of large language models (LLMs), specifically ChatGPT, in annotating MT outputs based on an error typology. In contrast to previous work focusing mainly on general language, we explore ChatGPT's ability to identify and categorise errors in specialised translations. By testing two different prompts and based on a customised error typology, we compare ChatG...

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...

Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events

We present a Bayesian dynamic borrowing (BDB) approach to enhance the quantitative identification of adverse events (AEs) in spontaneous reporting s...

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...

Seedream 3.0 Technical Report

We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements t...

Token-Level Constraint Boundary Search for Jailbreaking Text-to-Image Models

Recent advancements in Text-to-Image (T2I) generation have significantly enhanced the realism and creativity of generated images. However, such powe...

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...

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of tra...

A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification

In many medical imaging tasks, convolutional neural networks (CNNs) efficiently extract local features hierarchically. More recently, vision transfo...

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...

PETNet -- Coincident Particle Event Detection using Spiking Neural Networks

Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural netw...

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley va...

Concept Extraction for Time Series with ECLAD-ts

Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality predictio...

Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation

Deep learning has been successfully applied to medical image segmentation, enabling accurate identification of regions of interest such as organs an...

Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide

Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data f...

Opening the Black-Box: Symbolic Regression with Kolmogorov-Arnold Networks for Energy Applications

While most modern machine learning methods offer speed and accuracy, few promise interpretability or explainability -- two key features necessary fo...

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...