Multi-task deep learning for medical image computing and analysis: A review

Y Zhao, X Wang, T Che, G Bao, S Li - Computers in Biology and Medicine, 2023 - Elsevier
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

[HTML][HTML] Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey

S Tomassini, N Falcionelli, P Sernani, L Burattini… - Computers in Biology …, 2022 - Elsevier
Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, developing non-invasive systems to classify lung cancer histological …

Self-supervised transfer learning based on domain adaptation for benign-malignant lung nodule classification on thoracic CT

H Huang, R Wu, Y Li, C Peng - IEEE Journal of Biomedical and …, 2022 - ieeexplore.ieee.org
The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …

Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT

R Wu, C Liang, Y Li, X Shi, J Zhang, H Huang - Expert Systems with …, 2023 - Elsevier
Lung cancer is one of the most fatal malignant diseases, which poses an acute menace to
human health and life. The accurate differential diagnosis of lung nodules is a vital step in …

Towards machine learning-aided lung cancer clinical routines: Approaches and open challenges

F Silva, T Pereira, I Neves, J Morgado… - Journal of Personalized …, 2022 - mdpi.com
Advancements in the development of computer-aided decision (CAD) systems for clinical
routines provide unquestionable benefits in connecting human medical expertise with …

Benchmarking multi-task learning for sentiment analysis and offensive language identification in under-resourced dravidian languages

A Hande, SU Hegde, R Priyadharshini… - arXiv preprint arXiv …, 2021 - arxiv.org
To obtain extensive annotated data for under-resourced languages is challenging, so in this
research, we have investigated whether it is beneficial to train models using multi-task …

Effective lung nodule detection using deep CNN with dual attention mechanisms

Z UrRehman, Y Qiang, L Wang, Y Shi, Q Yang… - Scientific Reports, 2024 - nature.com
Novel methods are required to enhance lung cancer detection, which has overtaken other
cancer-related causes of death as the major cause of cancer-related mortality. Radiologists …

Pulmonary nodule classification using feature and ensemble learning-based fusion techniques

M Muzammil, I Ali, IU Haq, M Amir, S Abdullah - IEEE Access, 2021 - ieeexplore.ieee.org
The Pulmonary nodule indicates the presence of lung cancer. The deep convolutional
neural networks (DCNNs) have been widely used to classify the pulmonary nodule as …

Pulmonary nodule detection using 3-d residual u-net oriented context-guided attention and multi-branch classification network

H Yuan, Y Wu, J Cheng, Z Fan, Z Zeng - Ieee Access, 2021 - ieeexplore.ieee.org
Accurate detection of pulmonary nodules on chest computed tomography scans is crucial to
early diagnosis of lung cancer. To address the thorn problems on low detection sensitivity …

Reconstruction-assisted feature encoding network for histologic subtype classification of non-small cell lung cancer

H Li, Q Song, D Gui, M Wang, X Min… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Accurate histological subtype classification between adenocarcinoma (ADC) and squamous
cell carcinoma (SCC) using computed tomography (CT) images is of great importance to …