Pneumonia

OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-Adjustment

Contrastive Language-Image Pre-Training (CLIP) has enabled zero-shot classification in radiology, reducing reliance on manual annotations. However, conventional contrastive learning struggles with normal case detection due to its strict intra-sample alignment, which disrupts normal sample clustering and leads to high false positives (FPs) and false negatives (FNs). To address these issues, we pr...

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data

Does GPT know you? The answer depends on your level of public recognition; however, if your information was available on a website, the answer is probably yes. All Large Language Models (LLMs) memorize training data to some extent. If an LLM training corpus includes personal data, it also memorizes personal data. Developing an LLM typically involves processing personal data, which falls directly...

Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images

Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automati...

Meta-MolNet: A Cross-Domain Benchmark for Few Examples Drug Discovery.

Predicting the pharmacological activity, toxicity, and pharmacokinetic properties of molecules is a central task in drug discovery. Existing machine l...

Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.

Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent exa...

PaliGemma-CXR: A Multi-task Multimodal Model for TB Chest X-ray Interpretation

Tuberculosis (TB) is a infectious global health challenge. Chest X-rays are a standard method for TB screening, yet many countries face a critical s...

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Analysis of Québec Administrative Data.

The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-...

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year participa...

An Integrated Deep Learning Framework Leveraging NASNet and Vision Transformer with MixProcessing for Accurate and Precise Diagnosis of Lung Diseases

The lungs are the essential organs of respiration, and this system is significant in the carbon dioxide and exchange between oxygen that occurs in h...

CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understanding

Vision-language models have proven to be of great benefit for medical image analysis since they learn rich semantics from both images and reports. P...

Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

Automated radiology report generation offers an effective solution to alleviate radiologists' workload. However, most existing methods focus primari...

Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures

The COVID-19 pandemic strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribut...

Anatomical grounding pre-training for medical phrase grounding

Medical Phrase Grounding (MPG) maps radiological findings described in medical reports to specific regions in medical images. The primary obstacle h...

CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization

A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from ...

Community Detection in Multimodal Data: A Similarity Network Perspective

Similarity network construction is a fundamental step in many approaches to community detection in biomedical analysis. It is utilised both in the c...

Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challeng...

Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge

Estimating the construction year of buildings is of great importance for sustainability. Sustainable buildings minimize energy consumption and are a...

Enhancing Chest X-ray Classification through Knowledge Injection in Cross-Modality Learning

The integration of artificial intelligence in medical imaging has shown tremendous potential, yet the relationship between pre-trained knowledge and...

MaxSup: Overcoming Representation Collapse in Label Smoothing

Label Smoothing (LS) is widely adopted to curb overconfidence in neural network predictions and enhance generalization. However, previous research s...

Make Making Sustainable: Exploring Sustainability Practices, Challenges, and Opportunities in Making Activities

The recent democratization of personal fabrication has significantly advanced the maker movement and reshaped applied research in HCI and beyond. Ho...