12 citations · 36 across the 15 of their papers we have counts for
16 papers
For Those Who Believe in Faithfulness: Optimizing the Area Under Insertion and Deletion Curves for Ranking Relative Feature Importance
Bjørn Leth Møller, Bulat Ibragimov, Christian Igel
The adoption of machine learning for socially relevant tasks requires effective explainable artificial intelligence (XAI) methods to better understand the behavior of machine learn…
Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation
Leila Khaertdinova, Anna Anikina, Claudia Mello-Thoms +1
Accurate interpretation of volumetric CT requires efficient navigation of 3D image volumes and attention to diagnostically relevant regions. While eye-tracking has been widely stud…
Visual Search Patterns in 3D Pancreatic Imaging: An Eye Tracking Study
Anna Anikina, Leila Khaertdinova, Trine Balschmidt +6
Eye tracking has emerged as a powerful tool for examining visual perception and search strategies in various domains, including medicine. While it is relatively straightforward to…
MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models
Shengyuan Liu, Zanting Ye, Yunrui Lin +6
While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remai…
GPU Memory and Utilization Estimation for Training-Aware Resource Management: Opportunities and Limitations
Ehsan Yousefzadeh-Asl-Miandoab, Reza Karimzadeh, Danyal Yorulmaz +2
Collocating deep learning training tasks improves GPU utilization but risks resource contention, severe slowdowns, and out-of-memory (OOM) failures. Accurate memory estimation is e…
rNCA: Self-Repairing Segmentation Masks
Malte Silbernagel, Albert Alonso, Jens Petersen +3
Accurately predicting topologically correct masks remains a difficult task for general segmentation models, which often produce fragmented or disconnected outputs. Fixing these art…
Extremal Contours: Gradient-driven contours for compact visual attribution
Reza Karimzadeh, Albert Alonso, Frans Zdyb +2
Faithful yet compact explanations for vision models remain a challenge, as commonly used dense perturbation masks are often fragmented and overfitted, needing careful post-processi…
Gaze-Assisted Medical Image Segmentation
Leila Khaertdinova, Ilya Pershin, Tatiana Shmykova +1
The annotation of patient organs is a crucial part of various diagnostic and treatment procedures, such as radiotherapy planning. Manual annotation is extremely time-consuming, whi…
Building an AI Support Tool for Real-time Ulcerative Colitis Diagnosis
Bjørn Leth Møller, Bobby Zhao Sheng Lo, Johan Burisch +4
Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of…
Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
Evi M. C. Huijben, Maarten L. Terpstra, Arthur Jr. Galapon +56
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomogra…
Cross-Modal Conceptualization in Bottleneck Models
Danis Alukaev, Semen Kiselev, Ilya Pershin +4
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 classificatio…
A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays
Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh +2
This paper describes our solution for the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge at MICCAI 2023. Our approach consists of a multi-step framework tailored to…
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik +29
Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-ba…
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation
Sepideh Amiri, Bulat Ibragimov
Numerous oncology indications have extensively quantified metabolically active tumors using positron emission tomography (PET) and computed tomography (CT). F-fluorodeoxyglucose-po…
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
Aleksandr Nesterov, Bulat Ibragimov, Dmitriy Umerenkov +3
The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. The basic symptom checking systems based on…
Minimal Variance Sampling in Stochastic Gradient Boosting
Bulat Ibragimov, Gleb Gusev
Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each…