Dermatology

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases.

BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma.

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the applicability of deep learning-based anomaly detection to identify anomalous changes in negative breast CE-MRI screens associated with future lesion emer...

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutatio...

A deep learning framework for quantitative analysis of actin microridges.

Microridges are evolutionarily conserved actin-rich protrusions present on the apical surface of squamous epithelial cells. In zebrafish epidermal cel...

MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.

Distinguishing malignant from benign lesions has significant clinical impacts on both early detection and optimal management of those early detections...

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmissi...

Deep learning in computational dermatopathology of melanoma: A technical systematic literature review.

Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the ...

Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine.

OBJECTIVES: To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical sp...

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the res...

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by...

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.

Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). Howe...

Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix.

Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentat...

HER2-low breast cancer: insights on pathological testing.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for predicting prognosis and effectiveness of HER2-targeted therapy in breas...

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluat...

Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis.

Early melanoma diagnosis is the most important factor in the treatment of skin cancer and can effectively reduce mortality rates. Recently, Generative...

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection an...

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation.

Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification....

Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status.

PURPOSE: The detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for b...

Rema-Net: An efficient multi-attention convolutional neural network for rapid skin lesion segmentation.

For clinical treatment, the accurate segmentation of lesions from dermoscopic images is extremely valuable. Convolutional neural networks (such as U-N...

Bifunctional robots inducing targeted protein degradation.

The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attent...