Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
MATR: Multimodal medical image fusion via multiscale adaptive transformer
Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that
simultaneously contains functional metabolic information and structural tissue details …
simultaneously contains functional metabolic information and structural tissue details …
Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images
V Andrearczyk, V Oreiller, S Boughdad… - 3D head and neck tumor …, 2021 - Springer
This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation
S Zhang, J Zhang, B Tian, T Lukasiewicz, Z Xu - Medical Image Analysis, 2023 - Elsevier
Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …
few labeled data, but most of them only consider single-modal data. The excellent …
FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection
The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
Deep learning methods for medical image fusion: A review
T Zhou, QR Cheng, HL Lu, Q Li, XX Zhang… - Computers in Biology and …, 2023 - Elsevier
The image fusion methods based on deep learning has become a research hotspot in the
field of computer vision in recent years. This paper reviews these methods from five aspects …
field of computer vision in recent years. This paper reviews these methods from five aspects …
Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
I Domingues, G Pereira, P Martins, H Duarte… - Artificial Intelligence …, 2020 - Springer
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
FTransCNN: Fusing Transformer and a CNN based on fuzzy logic for uncertain medical image segmentation
The accurate segmentation of medical images plays a crucial role in diagnosing and treating
diseases. Although many methods now use multimodal joint segmentation, the joint use of …
diseases. Although many methods now use multimodal joint segmentation, the joint use of …