AIMC Topic: Deep Learning

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Predicting Immune Checkpoint Inhibitor-Related Pneumonitis via Computed Tomography and Whole-Lung Analysis Deep Learning.

Current medical imaging
BACKGROUND: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse event of immunotherapy. However, there is a lack of methods to identify patients who have a high risk of developing ICI-P in immunotherapy.

[Application of artificial intelligence methods in the diagnosis and treatment of primary angle-closure disease].

Vestnik oftalmologii
This article reviews literature on the use of artificial intelligence (AI) methods for the diagnosis and treatment of primary angle-closure disease (PACD). The review describes how AI techniques enhance the efficiency of population screening for ante...

Advanced Lung Disease Detection: CBAM-Augmented, Lightweight EfficientNetB2 with Visual Insights.

Current medical imaging
INTRODUCTION: This paper presents a multichannel deep-learning method for detecting lung diseases using chest X-ray images. Using EfficientNetB0 through EfficientNetB7 pretrained models, the methodology offers improved performance in classifying COVI...

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

Advances in experimental medicine and biology
The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in thi...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Advances in experimental medicine and biology
Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (...

Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

Advances in experimental medicine and biology
In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques and the influential open benchmark set by the BraTS challenge. This chapter provides an overview of...

Artificial Intelligence in Brain Tumors.

Advances in experimental medicine and biology
The introduction of "intelligent machines" goes back to Alan Turing in the 1940s. Artificial intelligence (AI) is a broad umbrella covering different methodologies, such as machine learning and deep learning. Deep learning, characterized by multilaye...

Large Language Models in Neurosurgery.

Advances in experimental medicine and biology
A large language model (LLM), in the context of natural language processing and artificial intelligence, refers to a sophisticated neural network that has been trained on a massive amount of text data to understand and generate human-like language. T...

Navigating Mathematical Basics: A Primer for Deep Learning in Science.

Advances in experimental medicine and biology
We present a gentle introduction to elementary mathematical notation with the focus of communicating deep learning principles. This is a "math crash course" aimed at quickly enabling scientists with understanding of the building blocks used in many e...

Deep Learning: A Primer for Neurosurgeons.

Advances in experimental medicine and biology
This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning ...