|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'506'133
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
End-to-End Discriminative Deep Network for Liver Lesion Classification | | Francisco Perdigon Romero
; Andre Diler
; Gabriel Bisson-Gregoire
; Simon Turcotte
; Real Lapointe
; Franck Vandenbroucke-Menu
; An Tang
; Samuel Kadoury
; | | Date: |
28 Jan 2019 | | Abstract: | Colorectal liver metastasis is one of most aggressive liver malignancies.
While the definition of lesion type based on CT images determines the diagnosis
and therapeutic strategy, the discrimination between cancerous and
non-cancerous lesions are critical and requires highly skilled expertise,
experience and time. In the present work we introduce an end-to-end deep
learning approach to assist in the discrimination between liver metastases from
colorectal cancer and benign cysts in abdominal CT images of the liver. Our
approach incorporates the efficient feature extraction of InceptionV3 combined
with residual connections and pre-trained weights from ImageNet. The
architecture also includes fully connected classification layers to generate a
probabilistic output of lesion type. We use an in-house clinical biobank with
230 liver lesions originating from 63 patients. With an accuracy of 0.96 and a
F1-score of 0.92, the results obtained with the proposed approach surpasses
state of the art methods. Our work provides the basis for incorporating machine
learning tools in specialized radiology software to assist physicians in the
early detection and treatment of liver lesions. | | Source: | arXiv, 1901.9483 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER