Building an AI Support Tool for Real-time Ulcerative Colitis Diagnosis

no code implementations • 10 Apr 2024 • Bjørn Leth Møller, Bobby Zhao Sheng Lo, Johan Burisch, Flemming Bendtsen, Ida Vind, Bulat Ibragimov, Christian Igel

We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability.

Decision Making

Cross-Modal Conceptualization in Bottleneck Models

2 code implementations • 23 Oct 2023 • Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov

Concept Bottleneck Models (CBMs) assume that training examples (e. g., x-ray images) are annotated with high-level concepts (e. g., types of abnormalities), and perform classification by first predicting the concepts, followed by predicting the label relying on these concepts.

Disentanglement

A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays

1 code implementation • 31 Aug 2023 • Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh, Lars Bjorndal, Bulat Ibragimov

In the first stage, we employed a Faster-RCNN model for detecting and identifying teeth.

Lesion Detection

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

1 code implementation • 29 May 2023 • Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Hoyeon Lim, Minchang Kim, Minkyung Lee, Minyoung Chung, Yeong-Gil Shin, Mathieu Leclercq, Lucia Cevidanes, Juan Carlos Prieto, Shaojie Zhuang, Guangshun Wei, Zhiming Cui, Yuanfeng Zhou, Tudor Dascalu, Bulat Ibragimov, Tae-Hoon Yong, Hong-Gi Ahn, Wan Kim, Jae-Hwan Han, Byungsun Choi, Niels van Nistelrooij, Steven Kempers, Shankeeth Vinayahalingam, Julien Strippoli, Aurélien Thollot, Hugo Setbon, Cyril Trosset, Edouard Ladroit

To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans.

Anatomy Segmentation

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset

no code implementations • Medical Physics 2023 • Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec

Potential applications The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN.

Benchmarking Computed Tomography (CT) +1

Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation

1 code implementation • 14 Oct 2022 • Sepideh Amiri, Bulat Ibragimov

Using the newly trained U-Net and Swin U-Netr results, we defined an optimal set of coefficients for test-time augmentation and utilized them in combination with a pre-trained fixed nnU-Net.

Computed Tomography (CT) Lesion Segmentation +2

NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization

1 code implementation • 2 Jun 2022 • Aleksandr Nesterov, Bulat Ibragimov, Dmitriy Umerenkov, Artem Shelmanov, Galina Zubkova, Vladimir Kokh

The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition.

Reinforcement Learning (RL)

Learn Together, Stop Apart: a Novel Approach to Ensemble Pruning

no code implementations • 29 Sep 2021 • Bulat Ibragimov, Gleb Gennadjevich Gusev

Gradient boosting is the most popular method of constructing ensembles that allow getting state-of-the-art results on many tasks.

Ensemble Pruning

Minimal Variance Sampling in Stochastic Gradient Boosting

no code implementations • NeurIPS 2019 • Bulat Ibragimov, Gleb Gusev

Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees.