Dr. Paromita Roy Profile

Dr. Paromita Roy

at Tata Medical Ctr

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 April 2024 Presentation + Paper

Efficient grading of prostate cancer WSI with deep learning

Riddhasree Bhattacharyya, Paromita Roy, Sugata Banerji, Sushmita Mitra

Proceedings Volume 12933, 1293305 (2024) https://doi.org/10.1117/12.3006835

KEYWORDS: Prostate cancer, Image classification, Prostate, Biopsy, RGB color model, Feature extraction, Classification systems, Convolutional neural networks, Deep convolutional neural networks, Deep learning, Computer vision technology

Read Abstract +

The field of histopathology, which involves visual examination of tissue samples at a microscopic scale, is very important for the diagnosis of cancer. Although this task is currently performed by human experts, the design of computer vision-based systems to assist human experts is an interesting research area. This problem is ideal for the application of computer-based image analysis; especially, with the great success of convolutional neural networks (CNNs) in image segmentation and classification in the last decade. However, applying CNNs to this problem is challenging for a number of reasons, such as excessive high resolution (involving huge computational burden), variations in sample processing, and insufficient annotation. In this current work, we propose a CNN-based approach to tackle the problem of prostate cancer grading from Whole Slide Images (WSIs). We use a patch-based, multi-step training algorithm to address the challenges of large image size, tissue sample variations and partial annotation. Then we propose two novel classification strategies using an ensemble of CNN models to classify tissue slide images into different ISUP grades (1 – 5). We demonstrate the efficacy of our method on the publicly available large scale Prostate cANcer graDe Assessment (PANDA) Challenge dataset. The effectiveness of the technique is measured using Cohen’s quadratic kappa score. The results are shown to be highly accurate (kappa score of 0.88) and better than other leading state-of-the art methods.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years