A5046780958- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging and Analysis
- Dental Radiography and Imaging
- IoT and Edge/Fog Computing
- Neuroinflammation and Neurodegeneration Mechanisms
- Olfactory and Sensory Function Studies
- Colorectal Cancer Screening and Detection
- RNA Research and Splicing
- Artificial Intelligence in Healthcare and Education
- Neurogenesis and neuroplasticity mechanisms
- Immune cells in cancer
- Tryptophan and brain disorders
- Advanced Neural Network Applications
- Geological Modeling and Analysis
- Lung Cancer Diagnosis and Treatment
- Medical Image Segmentation Techniques
- Biochemical Analysis and Sensing Techniques
- Single-cell and spatial transcriptomics
- Sleep and Wakefulness Research
- COVID-19 diagnosis using AI
- Topic Modeling
- Medical Imaging Techniques and Applications
- Genomics and Chromatin Dynamics
Istanbul Medipol University
2022-2025
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,...
Abstract RNA velocity exploits the temporal information contained in spliced and unspliced counts to infer transcriptional dynamics. Existing models often rely on coarse biophysical simplifications or numerical approximations solve underlying ordinary differential equations (ODEs), which can compromise accuracy challenging settings, such as complex weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of based linearization ODE, allows solving...
L-Theanine is commonly used to improve sleep quality through inhibitory neurotransmitters. On the other hand, Mg2+, a natural NMDA antagonist and GABA agonist, has critical role in regulation. Using caffeine-induced brain electrical activity model, here we investigated potency of L-theanine two novel Mg-L-theanine compounds with different magnesium concentrations on electrocorticography (ECoG) patterns, GABAergic serotonergic receptor expressions, dopamine, serotonin, melatonin levels....
<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...
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,...