Results 271 to 280 of about 29,416,981 (354)

MSFusion: Multi-Scale Cross-Modal Fusion with Adaptive Attention for Multimodal Medical Image Fusion. [PDF]

Biosensors (Basel)
Wang L, Zhou Y, Li W, Shi L.
europepmc   +1 more source

Application of transformer models in medical image segmentation: a narrative review. [PDF]

Quant Imaging Med Surg
Xu Y, Peng Y, Zhang C, Jiang K, She X, Feng L.   +5 more
europepmc   +1 more source

Generative Adversarial Network and Chaotic Map-Based Multi-Layer Medical Image Encryption. [PDF]

Sensors (Basel)
Erdoğan KD, Doğan N.
europepmc   +1 more source

Label tree semantic losses for rich multi-class medical image segmentation. [PDF]

Front Artif Intell
Wang J, MacCormac O, Rochford W, Kujawa A, Shapey J, Vercauteren T.   +5 more
europepmc   +1 more source

TAPGFusion: Anatomy-Aware Triple-Attention and MRI-Conditioned Prior Learning for Multimodal Medical Image Fusion. [PDF]

Biosensors (Basel)
Wang L, Zhou Y, Li W, Zhao J.
europepmc   +1 more source

Selecting Performance-Representative Validation Sets for Developing Medical Image Segmentation Neural Networks

Wu X, Yan W, Yi W, Bonmati E, Hu Y.
europepmc   +1 more source

RETRACTION: Recombinant Nanobody Against Muc1 Tandem Repeats Inhibits Growth, Invasion, Metastasis, and Vascularization of Spontaneous Mouse Mammary Tumors

Molecular Oncology, EarlyView.
RETRACTION: P. Merikhian, B. Darvishi, N. Jalili, M. R. Esmailinejad, A. S. Khatibi, S. M. Kalbolandi, M. Salehi, M. Mosayebzadeh, M. S. Barough, K. Majidzadeh‐A, F. Yadegari, F. Rahbarizadeh, and L. Farahmand, “Recombinant Nanobody Against Muc1 Tandem Repeats Inhibits Growth, Invasion, Metastasis, and Vascularization of Spontaneous Mouse Mammary ...
wiley   +1 more source

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UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Huimin Huang, Lan-Fen Lin, Ruo-Feng Tong, Hong-Jie Hu, Qiao-Wei Zhang, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Jian Wu   +8 more
semanticscholar   +1 more source

Medical Image Segmentation via Cascaded Attention Decoding

IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023
Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels.
M. Rahman, R. Marculescu
semanticscholar   +1 more source

Segmentation of medical images

Image and Vision Computing, 1993
Abstract Segmentation and labelling remains the weakest step in many medical vision applications. This paper illustrates an approach based on generic modules which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters.
Rudi Deklerck, Jan Cornelis 0001, Michel Bister   +2 more
openaire   +3 more sources