Digital and Computational Pathology: Bring the Future into Focus

Sylvia, L.; Pantanowitz, Liron; Parwani, Anil V.; Laak, Jeroen van der; Ung, Christopher; Balis, Ulysses J.; Isaacs, Mike; Glassy, Eric F.; Manning, Lisa

doi:10.4103/2153-3539.255259

Cited by 13 publications

(1 citation statement)

References 57 publications

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“…Table 4 displays BraTS 2020 validate set findings, Newsome (2006) proposed an improved 3DU-Net by using dilated convolutions ( Erus et al, 2020 ) and adding attention in skip connections ( Boveiri et al, 2020 ), which showed significant performance advantages compared to the original 3DU-Net ( Bui et al, 2019 ). Bernal (2002) added an EMA module to the lateral connections of the DMF-Net model ( Bao et al, 2003 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

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According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

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