Characterization of III/V Semiconductors on Silicon by Analyzing 4D-STEM Data with Convolutional Neural Networks

Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.

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Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma

Scientific Reports ◽

10.1038/s41598-020-71364-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Korosh Khalili ◽

Raymond L. Lawlor ◽

Marina Pourafkari ◽

Hua Lu ◽

Pascal Tyrrell ◽

...

Keyword(s):

Neural Networks ◽

Colorectal Carcinoma ◽

Convolutional Neural Networks ◽

Lower Number ◽

Ct Scans ◽

Reference Standard ◽

Diagnostic Confidence ◽

Diagnostic Challenge ◽

Supervised Training

Abstract Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characterizing small hypoattenuating hepatic nodules (SHHN) in colorectal carcinoma (CRC) on CT scans. Retrospective review of CRC CT scans over 6-years yielded 199 patients (550 SHHN) defined as < 1 cm in diameter. The reference standard was established through 1-year stability/MRI for benign or nodule evolution for malignant nodules. Five CNNs underwent supervised training on 150 patients (412 SHHN). The remaining 49 patients (138 SHHN) were used as testing-set to compare performance of 3 radiologists to CNN, measured through ROC AUC analysis of confidence rating assigned to each nodule by the radiologists. Multivariable modeling was used to compensate for radiologist bias from visible findings other than SHHN. In characterizing SHHN as benign or malignant, the radiologists’ mean AUC ROC (0.96) was significantly higher than CNN (0.84, p = 0.0004) but equivalent to CNN adjusted through multivariable modeling for presence of synchronous ≥ 1 cm liver metastases (0.95, p = 0.9). The diagnostic confidence of radiologists and CNN were analyzed. There were significantly lower number of nodules rated with low confidence by CNN (19.6%) and CNN with liver metastatic status (18.1%) than two (38.4%, 44.2%, p < 0.0001) but not a third radiologist (11.1%, p = 0.09). We conclude that in CRC, CNN in combination with liver metastatic status equaled expert radiologists in characterizing SHHN but with better diagnostic confidence.

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Characterization of Melanoma Using Convolutional Neural Networks and Dermoscopic Images

Lecture Notes in Electrical Engineering - Proceedings of the 4th International Conference on Electrical Engineering and Control Applications ◽

10.1007/978-981-15-6403-1_80 ◽

2020 ◽

pp. 1147-1155

Author(s):

Abdelghani Tafsast ◽

Mohamed Laid Hadjili ◽

Ayache Bouakaz ◽

Nabil Benoudjit

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks

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Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Mechanics of Advanced Materials and Structures ◽

10.1080/15376494.2021.1982090 ◽

2021 ◽

pp. 1-17

Author(s):

Mahindra Rautela ◽

Armin Huber ◽

J. Senthilnath ◽

S. Gopalakrishnan

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Guided Waves ◽

Feature Fusion

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Quantitative Structural Analysis of Influenza Virus by Cryo-electron Tomography and Convolutional Neural Networks

10.1101/2021.12.09.472010 ◽

2021 ◽

Author(s):

Qiu Yu Huang ◽

Kangkang Song ◽

Chen Xu ◽

Daniel Bolon ◽

Jennifer P. Wang ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Vaccine Development ◽

Electron Tomography ◽

Signal To Noise Ratio ◽

H1n1 Influenza ◽

Influenza Viruses ◽

Processing Pipeline ◽

Cryo Electron Tomography

Influenza viruses pose severe public health threats; they cause millions of infections and tens of thousands of deaths annually in the US. Influenza viruses are extensively pleomorphic, in both shape and size as well as organization of viral structural proteins. Analysis of influenza morphology and ultrastructure can help elucidate viral structure-function relationships as well as aid in therapeutics and vaccine development. While cryo-electron tomography (cryoET) can depict the 3D organization of pleomorphic influenza, the low signal-to-noise ratio inherent to cryoET and extensive viral heterogeneity have precluded detailed characterization of influenza viruses. In this report, we developed a cryoET processing pipeline leveraging convolutional neural networks (CNNs) to characterize the morphological architecture of the A/Puerto Rico/8/34 (H1N1) influenza strain. Our pipeline improved the throughput of cryoET analysis and accurately identified viral components within tomograms. Using this approach, we successfully characterized influenza viral morphology, glycoprotein density, and conduct subtomogram averaging of HA glycoproteins. Application of this processing pipeline can aid in the structural characterization of not only influenza viruses, but other pleomorphic viruses and infected cells.

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Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) ◽

10.1109/isbi.2017.7950609 ◽

2017 ◽

Cited By ~ 9

Author(s):

Vaanathi Sundaresan ◽

Christopher P. Bridge ◽

Christos Ioannou ◽

J. Alison Noble

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Fetal Heart ◽

Ultrasound Images ◽

Fully Convolutional Neural Networks

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Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks

Sensors ◽

10.3390/s21186033 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6033

Author(s):

Nathan J. Knighton ◽

Brian K. Cottle ◽

Bailey E. B. Kelson ◽

Robert W. Hitchcock ◽

Frank B. Sachse

Keyword(s):

Neural Networks ◽

Light Scattering ◽

Convolutional Neural Networks ◽

Volume Fraction ◽

Biological Tissues ◽

Atrial Fibrosis ◽

Cardiac Tissues ◽

Tissue Constructs ◽

Light Scattering Spectroscopy

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500–1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions.

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