Oncology/Hematology

Skin Cancer

Latest AI and machine learning research in skin cancer for healthcare professionals.

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Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and ide...

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected...

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous l...

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital hist...

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence...

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expec...

T Cell Epitope Prediction and Its Application to Immunotherapy.

T cells play a crucial role in controlling and driving the immune response with their ability to dis...

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer ...

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitativ...

neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival.

BACKGROUND: Neoantigen based personalized immune therapies achieve promising results in melanoma and...

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

AIM: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical ...

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressiv...

An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models.

Dermatological problems are the most widely spread skin diseases amongst human beings. They can be i...

Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.

BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naev...

The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunoth...

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging...

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

In a time of rapid advances in science and technology, the opportunities for radiation oncology are ...

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and ...

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