Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks

Lee, Myunggi; Lee, Hyeogjin; Oh, Jiyong; Lee, Hak Jong; Kim, Seung Hyup; Kwak, Nojun

doi:10.1007/978-3-319-46681-1_3

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The average F1 score, Precision, and Recall for our model are all 0.9983, and for their model are 0.977, 0.976, and 0.977, respectively. Zhang et al [27] compared the performance of their model with other state-of-the-art methods mentioned in [39], which include LSTM, biLSTM, GRU, CNN (Three layers), and SVM. They showed that their model outperforms the best methods.…”

Section: Phase 2 -Classificationmentioning

confidence: 99%

Classifying Protein Families with Learned Compressed Representations

Dehghanpoor¹,

Afrasiabi²,

Fogel³

et al.

EPiC Series in Computing

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Classifying proteins into families is an important task when studying newly discovered proteins. If we can identify the family a protein belongs to, we can predict features without knowing the exact structure of such a protein.However, this grouping process is challenging. We propose a two-stage algorithm that classifies proteins into families by combining a dimensionality reduction technique using a variational autoencoder with learned fingerprint representations using a Convolutional Neural Network (CNN). Our models use fewer parameters than existing methods but perform better, with our variational autoencoder achieving 94% accuracy in reconstructing the most common amino acid in a sequence alignment, and the neural network provides 98-100% accuracy in classifying protein families. We developed a software framework to access our algorithms. All code and data are publicly available at https://github.com/ramindehghanpoor/CLI.

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Section: Phase 2 -Classificationmentioning

confidence: 99%

Classifying Protein Families with Learned Compressed Representations

Dehghanpoor¹,

Afrasiabi²,

Fogel³

et al.

EPiC Series in Computing

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Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images

Kittipongdaja

Siriborvornratanakul

2022

J Image Video Proc.

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Bosniak renal cyst classification has been widely used in determining the complexity of a renal cyst. However, it turns out that about half of patients undergoing surgery for Bosniak category III, take surgical risks that reward them with no clinical benefit at all. This is because their pathological results reveal that the cysts are actually benign not malignant. This problem inspires us to use recently popular deep learning techniques and study alternative analytics methods for precise binary classification (benign or malignant tumor) on Computerized Tomography (CT) images. To achieve our goal, two consecutive steps are required–segmenting kidney organs or lesions from CT images then classifying the segmented kidneys. In this paper, we propose a study of kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for efficiently extracting intra-slice and inter-slice features. Our models are trained and validated on the public data set from Kidney Tumor Segmentation (KiTS19) challenge in two different training environments. As a result, all experimental models achieve high mean kidney Dice scores of at least 95% on the KiTS19 validation set consisting of 60 patients. Apart from the KiTS19 data set, we also conduct separate experiments on abdomen CT images of four Thai patients. Based on the four Thai patients, our experimental models show a drop in performance, where the best mean kidney Dice score is 87.60%.

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Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks

Cited by 2 publications

References 10 publications

Classifying Protein Families with Learned Compressed Representations

Classifying Protein Families with Learned Compressed Representations

Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images

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