AIMC Topic: Melanoma
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Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence.
European journal of medical research
Jan 5, 2024
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to ac...
Robot-Assisted Pelvic Dissection for Enlarged Lymph Nodes in Melanoma Improves Recovery with Equivalent Oncological Outcomes to Open Pelvic Dissection.
Annals of surgical oncology
Jan 4, 2024
BACKGROUND: Robot-assisted pelvic lymph node dissection (rPLND) has been reported in heterogenous groups of patients with melanoma, including macroscopic or at-high-risk-for microscopic metastasis. With changing indications for surgery in melanoma, a...
Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes.
Annals of clinical and laboratory science
Jan 4, 2024
OBJECTIVE: Deep learning has been shown to be useful in detecting breast cancer metastases by analyzing whole slide images (WSI) of sentinel lymph nodes; however, it requires extensive analysis of all the lymph node slides. Our deep learning study at...
Artificial intelligence in the detection of skin cancer: State of the art.
Clinics in dermatology
Jan 3, 2024
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. ...
Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.
Journal of the European Academy of Dermatology and Venereology : JEADV
Dec 29, 2023
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...
Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.
Nature biomedical engineering
Dec 28, 2023
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of exp...
SkinViT: A transformer based method for Melanoma and Nonmelanoma classification.
PloS one
Dec 27, 2023
Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite...
An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.
Scientific reports
Dec 14, 2023
When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherite...
A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.
Scientific data
Oct 16, 2023
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of un...
Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study.
Journal of cancer research and clinical oncology
Sep 27, 2023
BACKGROUND: Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.
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