A5065769767- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Dental Radiography and Imaging
- Medical Imaging and Analysis
- Total Knee Arthroplasty Outcomes
- Orthopedic Infections and Treatments
- Computer Graphics and Visualization Techniques
- 3D Shape Modeling and Analysis
- Generative Adversarial Networks and Image Synthesis
- Medical Imaging Techniques and Applications
- Medical Image Segmentation Techniques
- Orthopaedic implants and arthroplasty
- Cell Image Analysis Techniques
- Topic Modeling
- Lung Cancer Diagnosis and Treatment
- Cerebrovascular and Carotid Artery Diseases
- Advanced X-ray and CT Imaging
- Shoulder Injury and Treatment
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare and Education
- Geological Modeling and Analysis
- Intracranial Aneurysms: Treatment and Complications
- Colorectal Cancer Screening and Detection
- Advanced Neural Network Applications
- Retinal Imaging and Analysis
Istanbul Medipol University
2021-2025
ETH Zurich
2024
University of Zurich
2023-2024
University of Pennsylvania
2023
Abstract In this paper, a new powerful deep learning framework, named as DENTECT, is developed in order to instantly detect five different dental treatment approaches and simultaneously number the dentition based on FDI notation panoramic X-ray images. This makes DENTECT first system that focuses identification of multiple treatments; namely periapical lesion therapy, fillings, root canal (RCT), surgical extraction, conventional extraction all which are accurately located within their...
Deep Learning (DL) has the potential to optimize machine learning in both scientific and clinical communities. However, greater expertise is required develop DL algorithms, variability of implementations hinders their reproducibility, translation, deployment. Here we present community-driven Generally Nuanced Framework (GaNDLF), with goal lowering these barriers. GaNDLF makes mechanism development, training, inference more stable, reproducible, interpretable, scalable, without requiring an...
A major challenge in computational research 3D medical imaging is the lack of comprehensive datasets. Addressing this issue, our study introduces CT-RATE, first dataset that pairs images with textual reports. CT-RATE consists 25,692 non-contrast chest CT volumes, expanded to 50,188 through various reconstructions, from 21,304 unique patients, along corresponding radiology text Leveraging we developed CT-CLIP, a CT-focused contrastive language-image pre-training framework. As versatile,...
<title>Abstract</title> While computer vision has achieved tremendous success with multimodal encoding and direct textual interaction images via chat-based large language models, similar advancements in medical imaging AI—particularly 3D imaging—have been limited due to the scarcity of comprehensive datasets. To address this critical gap, we introduce CT-RATE, first dataset that pairs corresponding reports. CT-RATE comprises 25,692 non-contrast chest CT scans from 21,304 unique patients....
Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential aid analysis of these X-rays, thereby improving accuracy dental diagnoses plans. Nevertheless, designing automated algorithms this purpose poses significant challenges, mainly due scarcity annotated data variations anatomical structure. To address issues, Dental Enumeration Diagnosis on Challenge...
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for evaluation volumetric changes. However, many tools are optimized analyzing healthy tissue. To enable scans containing pathological tissue, it is therefore required to restore tissue in areas. In this work, we explore and extend denoising diffusion models consistent inpainting 3D brain We modify state-of-the-art 2D, pseudo-3D, methods working image space,...
The Circle of Willis (CoW) is an important network arteries connecting major circulations the brain. Its vascular architecture believed to affect risk, severity, and clinical outcome serious neuro-vascular diseases. However, characterizing highly variable CoW anatomy still a manual time-consuming expert task. usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) computed tomography (CTA), but there exist limited public datasets with annotations on...
Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged critical need to alleviate the workload of radiologists. While machine learning facilitated for 2D medical imaging, extending this 3D been unexplored due computational complexity and data scarcity. We introduce first method generate specifically targeting chest CT volumes. Given absence comparable methods, we establish baseline using an advanced...
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form text prompts, incorporates a encoder and three key components: novel causal vision transformer for encoding CT volumes, text-image aligning tokens, text-conditional super-resolution diffusion model. Given absence of directly comparable methods in imaging, we established baselines with cutting-edge demonstrate our method's effectiveness. GenerateCT significantly outperforms these across all metrics....
Due to the necessity for precise treatment planning, use of panoramic X-rays identify different dental diseases has tremendously increased. Although numerous ML models have been developed interpretation X-rays, there not an end-to-end model that can problematic teeth with enumeration and associated diagnoses at same time. To develop such a model, we structure three distinct types annotated data hierarchically following FDI system, first labeled only quadrant, second quadrant-enumeration,...
Glioblastoma is a highly aggressive and lethal form of brain cancer. Magnetic resonance imaging (MRI) plays significant role in the diagnosis, treatment planning, follow-up glioblastoma patients due to its non-invasive radiation-free nature. The International Brain Tumor Segmentation (BraTS) challenge has contributed generating numerous AI algorithms accurately efficiently segment sub-compartments using four structural (T1, T1Gd, T2, T2-FLAIR) MRI scans. However, these sequences may not...
The postoperative range of motion is one the crucial factors indicating outcome Total Knee Arthroplasty (TKA). Although correlation between knee flexion and posterior condylar offset (PCO) controversial in literature, PCO maintains its importance on evaluation TKA. Due to limitations measurement, two novel parameters, ratio (PCOR) anterior (ACOR), were introduced. Nowadays, calculation PCOR ACOR plain lateral radiographs done manually by orthopedic surgeons. In this regard, we developed a...