113 citations · 115 across the 6 of their papers we have counts for
6 papers
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
Liangrui Pan, Hetian Wang, Lian Wang +5
The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a…
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Liangrui Pan, Boya Ji, Peng Xi +3
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view…
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
Liangrui Pan, Peng Zhang, Chalongrat Daengngam +1
In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. How…
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
Liangrui Pan, Pronthep Pipitsunthonsan, Chalongrat Daengngam +1
With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major…
Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network
Liangrui Pan, Pronthep Pipitsunthonsan, Chalongrat Daengngam +3
This paper proposes a new framework based on a wavelet transform and deep neural network for identifying noisy Raman spectrum since, in practice, it is relatively difficult to clas…
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
Liangrui Pan, Pronthep Pipitsunthonsan, Peng Zhang +3
In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL…