Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems

@article{DuranLopez2021WideD,
  title={Wide \& Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems},
  author={Lourdes Duran-Lopez and J. P. Dominguez-Morales and Daniel Gutierrez-Galan and Antonio Rios-Navarro and Angel Jim{\'e}nez-Fernandez and Saturnino Vicente Diaz and Alejandro Linares-Barranco},
  journal={Computers in biology and medicine},
  year={2021},
  volume={136},
  pages={
          104743
        }
}

Figures and Tables from this paper

Deep Learning for Detection of Prostate Tumors by Microscopic Cells and MRI

The experimental results carried out have shown the effectiveness of the proposed methods using deep learning architectures, to detect prostate cancer tumor from microscopic cells and MRI imaging.

MixPatch: A New Method for Training Histopathology Image Classifiers

The proposed MixPatch method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.

Detection of COVID-19 from chest radiology using histogram equalization combined with a CNN convolutional network

A new method of detection of the presence of this virus in patients was implemented based on deep learning using a deep learning model by convolutional neural network architecture (CNN) using a COVID-QU chest X- ray imaging database.

References

SHOWING 1-10 OF 25 REFERENCES

PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection

A novel Deep Learning based computer-aided diagnosis system able to analyze whole-slide histology images that are first patch-sampled and preprocessed using different filters, including a novel patch-scoring algorithm that removes worthless areas from the tissue.

Performance Evaluation of Deep Learning-Based Prostate Cancer Screening Methods in Histopathological Images: Measuring the Impact of the Model’s Complexity on Its Processing Speed

This work measured the performance of current state-of-the-art models for PCa detection with a novel benchmark and compared the results with PROMETEO, a custom architecture that is proposed, and shows that using dedicated models for specific applications could be of great importance in the future.

Deep learning in medical image analysis: a third eye for doctors.

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

A multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations, and has the ability to train accurate classification models at unprecedented scale.

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

It is found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention.

A survey on deep learning in medical image analysis

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

Prostate cancer: diagnosis and staging.

Improved clinical staging modalities are required for more reliable prediction of pathological stage and for monitoring of response to treatments.

Deep Learning for Medical Image Analysis

Different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation for analyzing medical images using deep learning algorithm are explored.