Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas.

@inproceedings{Vendittelli2022AutomaticTS,
  title={Automatic tumour segmentation in H\&E-stained whole-slide images of the pancreas.},
  author={Pierpaolo Vendittelli and Esther M. M. Smeets and Geert J. S. Litjens},
  booktitle={Medical Imaging},
  year={2022}
}
Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and… 

References

SHOWING 1-10 OF 14 REFERENCES

Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks

TLDR
This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset and demonstrates that the proposed model can effectively diagnose PDAC using histopathic images.

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

TLDR
This study developed convolutional neural networks to distinguish tissue from background and tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution.

U-Net: Convolutional Networks for Biomedical Image Segmentation

TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Neural Image Compression for Gigapixel Histopathology Image Analysis

TLDR
The proposed Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels, can exploit visual cues associated with image- level labels successfully, integrating both global and local visual information.

Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images

TLDR
This work proposes a novel method to directly train convolutional neural networks using any input image size end-to-end, and shows a proof of concept using images of up to 66-megapixels (8192×8192), saving approximately 50GB of memory per image.

Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors

TLDR
Up-to-date statistics on pancreatic cancer occurrence and outcome along with a better understanding of the etiology and identifying the causative risk factors are essential for the primary prevention of this disease.

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

TLDR
The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning versus transitioned countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy.

Cancer statistics, 2020

TLDR
Slow momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers, and it is notable that long‐term rapid increases in liver cancer mortality have attenuated in women and stabilized in men.