Dermatology

DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis

Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin lesions and improve diagnostic accuracy. However, existing AI models for skin disease diagnosis are often developed and tested on limited and biased datasets, le...

An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation

An often overlooked problem in medical image segmentation research is the effective selection of training subsets to annotate from a complete set of unlabelled data. Many studies select their training sets at random, which may lead to suboptimal model performance, especially in the minimal supervision setting where each training image has a profound effect on performance outcomes. This work aims...

Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification

Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these...

Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising

Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and r...

Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance

Purpose: Segmentation of the breast lesion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an essential step to accurately diag...

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.

The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and...

MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset

With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and tech...

Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net

Automatic tissue segmentation and nuclei detection is an important task in pathology, aiding in biomarker extraction and discovery. The panoptic seg...

Assessing the effects of immune checkpoint inhibitors on bone utilizing machine learning-assisted opportunistic quantitative computed tomography.

Immune checkpoint inhibitors (ICIs) are widely used in cancer treatment, yet their impact on bone health remains unclear. This study aimed to perform ...

Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation

Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmentin...

A Novel Framework for Comparing Combination Therapy Outcomes Using Mechanistic Graph Models

Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requirin...

[Scale-invariant feature-enhanced deep learning framework for oral mucosal lesion segmentation].

To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm ...

State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-...

ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation

Melanoma is a malignant tumor originating from skin cell lesions. Accurate and efficient segmentation of skin lesions is essential for quantitative ...

Interactive Segmentation and Report Generation for CT Images

Automated CT report generation plays a crucial role in improving diagnostic accuracy and clinical workflow efficiency. However, existing methods lac...

In-Depth Analysis of Automated Acne Disease Recognition and Classification

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital t...

BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.

RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic stra...

LesionDiffusion: Towards Text-controlled General Lesion Synthesis

Fully-supervised lesion recognition methods in medical imaging face challenges due to the reliance on large annotated datasets, which are expensive ...

LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging

In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing ...

Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions

Millions of melanocytic skin lesions are examined by pathologists each year, the majority of which concern common nevi (i.e., ordinary moles). While...