Dermatology

Determination of 5-fluorouracil anticancer drug solubility in supercritical COusing semi-empirical and machine learning models.

In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or na...

A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification.

Accurately and early diagnosis of melanoma is one of the challenging tasks due to its unique charact...

Artificial intelligence with ChatGPT 4: a large language model in support of ocular oncology cases.

PURPOSE: To evaluate ChatGPT's ability to analyze comprehensive case descriptions of patients with u...

Towards autonomous robotic THz-based in vivo skin sensing: the PicoBot.

Terahertz (THz) light has the unique properties of being very sensitive to water, non-ionizing, and ...

Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection.

Employing two standard mammography views is crucial for radiologists, providing comprehensive insigh...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of ob...

Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and predicti...

SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance...

Statin use and longitudinal bone marrow lesion burden: analysis of knees without osteoarthritis from the Osteoarthritis Initiative study.

OBJECTIVES: Knee subchondral bone marrow lesions (BMLs) are one of the hallmark features of structur...

Towards unbiased skin cancer classification using deep feature fusion.

This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the dete...

Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.

BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while ...

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.

BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the secon...

Utilizing AI to Improve Healthcare Access and Address Disparities in Dermatology.

Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address m...

Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia.

Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and...

User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.

BACKGROUND: Skin cancers, including melanoma and keratinocyte cancers, are among the most common can...

LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation.

In the field of medical science, skin segmentation has gained significant importance, particularly i...

Non-invasive Assessment of Human Epidermal Growth Factor Receptor 2 Expression in Gastric Cancer Based on Deep Learning: A Computed Tomography-based Multicenter Study.

RATIONALE AND OBJECTIVES: The expression of human epidermal growth factor receptor 2 (HER2) in gastr...

Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning.

BACKGROUND: There is a need to improve risk stratification of primary cutaneous melanomas to better ...

Comment on 'CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images'.

An analysis of the methodology used by the authors of the commented article is presented and errors ...

Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.

Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection ...