4 research outputs found
Microaneurysm detection using deep learning and interleaved freezing
Diabetes affects one in eleven adults. Diabetic retinopathy is a microvascular complication of diabetes and the
leading cause of blindness in the working-age population. Microaneurysms are the earliest clinical signs of diabetic
retinopathy. This paper proposes an automatic method for detecting microaneurysms in fundus photographies. A
novel patch-based fully convolutional neural network for detection of microaneurysms is proposed. Compared to
other methods that require five processing stages, it requires only two. Furthermore, a novel network fine-tuning
scheme called Interleaved Freezing is presented. This procedure significantly reduces the amount of time needed
to re-train a network and produces competitive results. The proposed method was evaluated using publicly
available and widely used datasets: E-Ophtha and ROC. It outperforms the state-of-the-art methods in terms of
free-response receiver operatic characteristic (FROC) metric. Simplicity, performance, efficiency and robustness
of the proposed method demonstrates its suitability for diabetic retinopathy screening applications
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis