A Novel Optimized Colonic adenocarcinoma Detection using Deep Transfer Learning Approach with XceptionTS Model

Ulcerative colitis with low-grade dysplasia

UK Fifth National Colorectal Cancer Consensus Meeting 2010

A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20 K for temperature and 0.2 dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars.

Associations of dietary fat types (MUFA, PUFA, SFA) and sources (animal, plant) with colorectal cancer risk: A comprehensive systematic review and dose-response meta-analysis of prospective cohort studies.

Association Between Body Size and Colorectal Adenoma Recurrence

Guidelines on genetic evaluation and management of Lynch syndrome: A consensus statement by the U.S. Multi-Society Task Force on Colorectal Cancer

Association of dietary insulinemic potential and colorectal cancer risk in men and women

In order to improve colorectal cancer (CRC) stratification, researches made before were using biomarkers, biomarker combinations or gene expression profile (GEP) clustering individually for patient classification. This study was trying to use both biomarker and GEP for the colon cancer (CC) classification. GEPs were adopted as an approach in selecting and combining biomarkers for patient classification. A practical research was made based on public dataset GSE40967, which contained GEP data of 566 CC patients, messages of tumor-node-metastasis (TNM) staging, biomarker (DNA mismatch repair [MMR], CpG island methylator phenotype [CIMP], chromosomal instability [CIN], KRAS, BRAF, TP53) statuses, patient sex, tumor location, chemotherapy status, and relapse-free survival (RFS). For each of the biomarkers, differentially expressed genes (DEGs) between two biomarker-statuses of patients were identified. The biomarker with the most DEGs was selected first and was used to classify patients. In each obtained class (or subclass), the remained biomarkers were used again to identify DEGs, to select next biomarker, to classify patients, and so on. These selected biomarkers were combined successively for the further classification. For stage II patients, MMR, CIN and KRAS were selected successively; for stage III patients, MMR and KRAS were selected successively. As a result, seven leaf-classes, II/dMMR, II/pMMR/CIN-, II/pMMR/CIN+/KRAS-wild-type, II/pMMR/CIN+/KRAS-mutant, III/dMMR, III/pMMR/KRAS-wild-type, and III/pMMR/KRAS-mutant were obtained. Significant RFS rate differences were observed between the leaf classes, especially in the case of stage III and non-chemotherapy, which validates the effectiveness of the classification. Large inner-stage differences were observed between the leaf classes of stage III in the case of non-chemotherapy, which might be helpful for future staging.

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer

Helicobacter pylori infection and colorectal carcinoma: pathologic aspects.

Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into these studies for data compensation due to channel mismatch. This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input. We employ the state-of-the-art SeqSleepNet and train the network in the source domain, i.e. the large dataset. Afterwards, the pretrained network is finetuned in the target domain, i.e. the small cohort, to complete knowledge transfer. We study two transfer learning scenarios with slight and heavy channel mismatch between the source and target domains. We also investigate whether, and if so, how finetuning entirely or partially the pretrained network would affect the performance of sleep staging on the target domain. Using the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and the Sleep-EDF Expanded database consisting of 20 subjects as the target domain in this study, our experimental results show significant performance improvement on sleep staging achieved with the proposed deep transfer learning approach. Furthermore, these results also reveal the essential of finetuning the feature-learning parts of the pretrained network to be able to bypass the channel mismatch problem.

Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can be exploited by cybercriminals and cause severe losses to the users. Informing users of their PII exposure in social media is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced techniques are needed to extract users’ exposed PII in social media automatically, whereas most existing studies remain manual. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media in order to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. An ablation analysis further confirms the efficacy of each component in our model. Our proposed framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, thereby protecting the privacy of internet users.

Power quality disturbances (PQDs) can significantly affect the reliability of electrical power systems, leading to potential equipment damage and operational inefficiencies. Accurate classification of these disturbances is essential for ensuring continuous and reliable service. The study proposes a deep transfer learning (TL) approach for PQD classification. In the proposed work, various single and multiple PQD signals pertaining to 15 different classes have been generated using mathematical models of PQDs adhering to the guidelines of the IEEE 1159 and IEC 61000-4-30 standards. Time-domain PQD signals are first converted into 2D color images using continuous wavelet transform (CWT). These images are then used to re-train modified pre-trained models such as GoogleNet, SqueezeNet, ResNet-18, and ShuffleNet on synthetic PQD data. Various single and combined PQDs are then classified using trained models. Moreover, the performance of the trained models is evaluated with the PQD signals containing noise of various signal-to-noise ratios (SNR), as well as PQD signals collected from the experimental setup in the laboratory. The results signify that the GoogleNet model exhibits consistent performance for classifying PQDs under various conditions, achieving classification accuracy of 99.8% for synthetic noiseless signals, 98.87% for signals with 20 dB SNR, and 98.89% for signals acquired through experimental setup. Furthermore, the trained models were tested using real-world PQD signals, comprising 26 sag and 42 impulse signals. The GoogleNet model achieved the highest classification accuracy, correctly identifying 23 sag and 34 impulse events, thereby demonstrating real-world applicability and robustness of the proposed approach.