Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
A survey on deep learning applied to medical images: from simple artificial neural networks to generative models
P Celard, EL Iglesias, JM Sorribes-Fdez… - Neural Computing and …, 2023 - Springer
Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …
in medical image analysis. This paper surveys fundamental deep learning concepts related …
Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
Fast unsupervised brain anomaly detection and segmentation with diffusion models
Deep generative models have emerged as promising tools for detecting arbitrary anomalies
in data, dispensing with the necessity for manual labelling. Recently, autoregressive …
in data, dispensing with the necessity for manual labelling. Recently, autoregressive …
Label-free liver tumor segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …
What is machine learning, artificial neural networks and deep learning?—Examples of practical applications in medicine
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …
Segment any anomaly without training via hybrid prompt regularization
We present a novel framework, ie, Segment Any Anomaly+(SAA+), for zero-shot anomaly
segmentation with hybrid prompt regularization to improve the adaptability of modern …
segmentation with hybrid prompt regularization to improve the adaptability of modern …
Label-free segmentation of COVID-19 lesions in lung CT
Scarcity of annotated images hampers the building of automated solution for reliable COVID-
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …
Segmentmeifyoucan: A benchmark for anomaly segmentation
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are
usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle …
usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle …
Anomaly detection in medical imaging-a mini review
ME Tschuchnig, M Gadermayr - International Data Science Conference, 2021 - Springer
The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …
improvements in detecting, visualizing and segmenting lesions, easing the workload for …