This chapter provides an introduction to deep learners and deep learner descriptors for medical applications. A basic outline of the deep learning (DL) process and five methods for exploiting DL with the Convolutional Neural Network (CNN), is presented, as well as a summary of the chapters in this book.

Hepatocellular carcinoma (HCC) is a significant cause of morbidity and mortality worldwide. Despite significant advancements in detection and treatment of HCC, its management remains a challenge. Artificial intelligence (AI) has played a role in medicine for several decades, however, clinically applicable AIdriven solutions have only started to emerge, due to gradual improvement in sensitivity and specificity of AI, and implementation of convoluted neural networks. A review of the existing literature has been conducted to determine the role of AI in HCC, and three main domains were identified in the search: detection, characterisation and prediction. Implementation of AI models into detection of HCC has immense potential, as AI excels at analysis and integration of large datasets. The use of biomarkers, with the rise of ‘-omics’, can revolutionise the detection of HCC. Tumour characterisation (differentiation between benign masses, HCC, and other malignant tumours, as well as staging and grading) using AI was shown to be superior to classical statistical methods, based on radiological and pathological images. Finally, AI solutions for predicting treatment outcomes and survival emerged in recent years with the potential to shape future HCC guidelines. These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice. However, AI research on HCC has several limitations, hindering its clinical adoption; small sample size, single-centre data collection, lack of collaboration and transparency, lack of external validation, and model overfitting all results in low generalisability of the results that currently exist. AI has potential to revolutionise detection, characterisation and prediction of HCC, however, for AI solutions to reach widespread clinical adoption, interdisciplinary collaboration is needed, to foster an environment in which AI solutions can be further improved, validated and included in treatment algorithms. In conclusion, AI has a multifaceted role in HCC across all aspects of the disease and its importance can increase in the near future, as more sophisticated technologies emerge.

methods

/pdf/artificial-intelligence-in-the-detection-characterisation-4w5nonnq9y.pdf

An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review

Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk Recently, deep learning (DL) based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges However, its effectiveness on pancreatic cancer SBRT is yet to be fully explored due to limited investigations in the literature This study aims to further current knowledge in DL-based dose prediction tasks by implementing and demonstrating the feasibility of a new dual pyramid networks (DPNs) integrated DL model for predicting dose distributions of pancreatic SBRT The proposed framework is composed of four parts: CT-only feature pyramid network (FPN), contour-only FPN, late fusion network and an adversarial network During each phase of the network, combination of mean absolute error, gradient difference error, histogram matching, and adversarial loss is used for supervision The performance of proposed model was demonstrated for pancreatic cancer SBRT plans with doses prescribed between 33 and 50 Gy across as many as three planning target volumes (PTVs) in five fractions Five-fold cross validation was performed on 30 patients, and another 20 patients were used as holdout tests of trained model Predicted plans were compared with clinically approved plans through dose volume parameters and two-paired t-test For the same sets, our results were compared with three different DL architectures: 3D U-Net, 3D U-Net with adversarial learning, and DPN without adversarial learning The proposed framework was able to predict 87% and 91% of clinically relevant dose parameters for cross validation sets and holdout sets, respectively, without any significant differences (P > 005) Dose distribution predicted by our framework was also able to predict the intentional hotspots as feature characteristics of SBRT plans Our method achieved higher correlation coefficients with the ground truth in 22/26, 24/26 and 20/26 dose volume parameters compared to the network without adversarial learning, 3D U-Net, and 3D U-Net with adversarial learning, respectively Overall, the proposed model was able to predict doses to cases with both single and multiple PTVs In conclusion, the DPN integrated DL model was successfully implemented, and demonstrated good dose prediction accuracy and dosimetric characteristics for pancreatic cancer SBRT

An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

Citations

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review

Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network.

Related Papers (5)

A Learning Transition from Machine Learning to Deep Learning: A Survey

Automated Evaluation of Learners with ODALA: Application to Relational Databases E-learning

Deep Learning: An Overview and Innovative Approach in Machine Learning

Learner Modeling to Facilitate Personalized E-Learning Experience.

AI education matters: teaching with deep learning frameworks in introductory machine learning courses