Tag

phd

16 entries all tags

Samenvatting 2022 thesis · Samenvatting Het proefschrift in het kort: AI die prostaatbiopten beoordeelt haalt het niveau van pathologen, en werkt nog beter zodra pathologen het zelf gebruiken.

Summary 2022 thesis · Summary The thesis in one page: AI trained to grade prostate biopsies reaches pathologist level, and improves further once pathologists use it themselves.

General discussion 2022 thesis · Ch. 6 A look back at the four studies and forward: how AI support fits a pathologist's workflow, and what diagnostics could look like beyond Gleason grading.

AI-assisted Gleason grading 2022 thesis · Ch. 5 Fourteen pathologists graded 160 biopsies with and without AI assistance. With the AI they agreed more with the expert reference, and beat the standalone system.

The PANDA Challenge 2022 thesis · Ch. 4 A global competition on 10,616 public biopsies drew 1,290 developers. The resulting algorithms matched pathologist-level grading on blinded US and EU cohorts.

Automated Gleason grading of prostate biopsies 2022 thesis · Ch. 3 We built a deep-learning system that grades prostate biopsies without manual annotations. It matched the expert consensus and outperformed 10 of 15 pathologists.

IHC-based epithelium segmentation 2022 thesis · Ch. 2 Restaining slides with immunohistochemistry gave us a precise ground truth. One network learned epithelium on IHC, a second learned it on H&E.

General introduction 2022 thesis · Ch. 1 How prostate cancer is diagnosed and graded, why Gleason grading varies so much between pathologists, and what deep learning brings to the problem.

AI for diagnosis of prostate cancer: the PANDA challenge Artificial intelligence (AI) for prostate cancer analysis is ready for clinical implementation, shows a global programming competition, the PANDA challenge.

Improve prostate cancer diagnosis: participate in the PANDA Gleason grading challenge Can you build a deep learning model that can accurately grade prostate biopsies? Participate in the PANDA challenge

The potential of AI in medicine: AI-assistance improves prostate cancer grading In a completely new study we investigated the possible benefits of an AI system for pathologists. Instead of focussing on pathologist-versus-AI, we instead look at potential pathologist-AI synergy.

Epithelium segmentation using deep learning and immunohistochemistry We developed a new deep learning method to segment epithelial tissue in digitized hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard.

The public PESO dataset: prostate H&E whole-side images for epithelium segmentation Public dataset of prostatectomy whole-slide images for epithelium segmentation, licensed under a BY-NC-SA Creative Commons license.

Pathologist-level Gleason grading using artificial intelligence (AI) & deep learning We developed a fully automated deep learning system to grade prostate biopsies using 5759 biopsies from 1243 patients, and showed that this system achieved pathologist-level performance.

Unsupervised Cancer Detection using Deep Learning and Adversarial Autoencoders Prostate cancer is graded based on distinctive patterns in the tissue. At MIDL2018 I presented an unsupervised deep learning method, based on clustering adversarial autoencoders, to train a system to detect prostate cancer without using labeled data.

Epithelium segmentation in H&E-stained prostate tissue using deep learning Building systems to detect tumor, in this case prostate cancer, is often hard due to a lack of data. Tumor annotations made by pathologists are often coarse due to time constraints. With this project we want to automatically refine these annotations by building a system that can automatically filter out irrelevant parts of the data.