Dermatology

MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammograms

Accurate lesion tracking in temporal mammograms is essential for monitoring breast cancer progression and facilitating early diagnosis. However, automated lesion correspondence across exams remains a challenges in computer-aided diagnosis (CAD) systems, limiting their effectiveness. We propose MammoTracker, a mask-guided lesion tracking framework that automates lesion localization across consecu...

MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation

Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning, where accurate boundary delineation is essential for precise lesion localization, organ identification, and quantitative assessment. In recent years, deep learning-based methods have significantly advanced segmentation accuracy. However, two major challenges remain. First, the performance of these metho...

VAP-Diffusion: Enriching Descriptions with MLLMs for Enhanced Medical Image Generation

As the appearance of medical images is influenced by multiple underlying factors, generative models require rich attribute information beyond labels...

Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning

White matter hyperintensities (WMH) are radiological markers of small vessel disease and neurodegeneration, whose accurate segmentation and spatial ...

GRASP-PsONet: Gradient-based Removal of Spurious Patterns for PsOriasis Severity Classification

Psoriasis (PsO) severity scoring is important for clinical trials but is hindered by inter-rater variability and the burden of in person clinical ev...

Comparing the number-needed-to-biopsy ratio for melanoma diagnosis by artificial intelligence as a medical device, teledermatology and face-to-face models of care.

Preparation of Active On-Demand Antibacterial Hydrogel Epidermis Electrodes Based on Flora Balance Strategy for Intelligent Prostheses.

Hydrogel epidermis electrodes have demonstrated remarkable potential for stable electrophysiological signal acquisition in the field of intelligent pr...

FTIR-based machine learning for prediction of malignant transformation in oral epithelial dysplasia.

Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a poor prognosis. Oral epithelial dysplasia (OED) is a precancerous lesion associated...

Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation

Background: Accurate lesion segmentation is critical for multiple sclerosis (MS) diagnosis, yet current deep learning approaches face robustness cha...

Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning.

Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected micr...

Simple is what you need for efficient and accurate medical image segmentation

While modern segmentation models often prioritize performance over practicality, we advocate a design philosophy prioritizing simplicity and efficie...

OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologies, but its detection effectiveness can be aided by...

A multimodal deep learning model for detecting endoscopic images of near-infrared fluorescence capsules.

Early screening for gastrointestinal (GI) diseases is critical for preventing cancer development. With the rapid advancement of deep learning technolo...

Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation

Accurate and reliable skin cancer diagnosis is critical for early treatment and improved patient outcomes. Deep learning (DL) models have shown prom...

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images

Accurate lesion segmentation in histopathology images is essential for diagnostic interpretation and quantitative analysis, yet it remains challengi...

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts

Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable, but they...

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conv...

PDSE: A Multiple Lesion Detector for CT Images using PANet and Deformable Squeeze-and-Excitation Block

Detecting lesions in Computed Tomography (CT) scans is a challenging task in medical image processing due to the diverse types, sizes, and locations...

Hybrid Ensemble of Segmentation-Assisted Classification and GBDT for Skin Cancer Detection with Engineered Metadata and Synthetic Lesions from ISIC 2024 Non-Dermoscopic 3D-TBP Images

Skin cancer is among the most prevalent and life-threatening diseases worldwide, with early detection being critical to patient outcomes. This work ...

Whole Brain 3D T1 Mapping in Multiple Sclerosis Using Standard Clinical Images Compared to MP2RAGE and MR Fingerprinting.

Quantitative T1 and T2 mapping is a useful tool to assess properties of healthy and diseased tissues. However, clinical diagnostic imaging remains dom...