AIMC Topic: Melanoma
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ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions.
Computer methods and programs in biomedicine
Jan 5, 2022
BACKGROUND AND OBJECTIVES: One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although common...
Deep learning data augmentation for Raman spectroscopy cancer tissue classification.
Scientific reports
Dec 13, 2021
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design...
A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.
Computational intelligence and neuroscience
Dec 6, 2021
In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early id...
Robotic Extended Ultrasound-Guided Distal Pancreatectomy for Pancreatic Metastases from Uveal Melanoma.
Annals of surgical oncology
Nov 27, 2021
BACKGROUND: Isolated pancreatic metastasis from melanoma is extremely uncommon and accounts for approximately only 2% of visceral disseminations of melanoma. Interestingly, pancreatic localizations disproportionately derive from primary ocular melano...
A disease network-based deep learning approach for characterizing melanoma.
International journal of cancer
Nov 17, 2021
Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) all...
Prognostic Value of Vitamin D Serum Levels in Cutaneous Melanoma.
Actas dermo-sifiliograficas
Nov 16, 2021
INTRODUCTION: Vitamin D plays a fundamental role in many metabolic pathways, including those involved in cell proliferation and the immune response. Serum levels of this vitamin have been linked to melanoma risk and prognosis. This study aimed to ass...
Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network.
Journal of healthcare engineering
Nov 1, 2021
At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable di...
Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.
The Journal of investigative dermatology
Oct 30, 2021
Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing who...
Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.
Journal of healthcare engineering
Oct 26, 2021
In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been re...
Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.
Scientific reports
Sep 30, 2021
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. ...
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