AIMC Topic: Image Processing, Computer-Assisted

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Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Trends in cancer
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most exten...

Radiological images and machine learning: Trends, perspectives, and prospects.

Computers in biology and medicine
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patter...

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Medical image analysis
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor prolifera...

Rice Blast Disease Recognition Using a Deep Convolutional Neural Network.

Scientific reports
Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice bl...

An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM.

Journal of medical systems
The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy ti...

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

PloS one
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...

Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks.

IEEE transactions on medical imaging
Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may...

Deep Attention Network for Egocentric Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recognizing a camera wearer's actions from videos captured by an egocentric camera is a challenging task. In this paper, we employ a two-stream deep neural network composed of an appearance-based stream and a motion-based stream to recognize egocentr...

Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).

Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.

Journal of medical systems
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization,...