A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities

1 code implementation • 26 Mar 2024 • Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, Emine Bensu Erdemir, Abdullah Alanbay, Anjany Sekuboyina, Berkan Lafci, Mehmet K. Ozdemir, Bjoern Menze

A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets.

Anomaly Detection Retrieval

CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging

1 code implementation • 11 Mar 2024 • Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze

Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation.

DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays

1 code implementation • 30 May 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Atif Emre Yuksel, Sadullah Gultekin, Serife Damla Ozdemir, Kaiyuan Yang, Hongwei Bran Li, Sarthak Pati, Bernd Stadlinger, Albert Mehl, Mustafa Gundogar, Bjoern Menze

To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023.

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

1 code implementation • 25 May 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboyina, Enis Simsar, Alperen Tezcan, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Furkan Almas, Irem Dogan, Muhammed Furkan Dasdelen, Chinmay Prabhakar, Hadrien Reynaud, Sarthak Pati, Christian Bluethgen, Mehmet Kemal Ozdemir, Bjoern Menze

As an example, we generated 100, 000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes.

Computed Tomography (CT) Image Generation +5

Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

2 code implementations • 11 Mar 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze

To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.

Denoising Object +2

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

1 code implementation • 26 Feb 2021 • Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, Spyridon Bakas

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.

Brain Segmentation Data Augmentation +2