AIMC Topic: Deep Learning

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Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images.

Radiological physics and technology
The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review boa...

Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing.

Biosensors & bioelectronics
Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex an...

From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping.

Medical image analysis
Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional...

Fine-Grained Image Analysis With Deep Learning: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate catego...

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends.

IEEE transactions on pattern analysis and machine intelligence
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imag...

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

IEEE transactions on pattern analysis and machine intelligence
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the di...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Proceedings of the National Academy of Sciences of the United States of America
Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly lim...

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning.

Sensors (Basel, Switzerland)
The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung dise...

Imaging and Deep Learning Based Approach to Leaf Wetness Detection in Strawberry.

Sensors (Basel, Switzerland)
The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS di...

Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.

Radiation oncology (London, England)
BACKGROUND: This paper attempts to conduct a systematic review and meta-analysis of deep learning (DLs) models for cervical cancer CT image segmentation.