AIMC Topic: Image Interpretation, Computer-Assisted
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Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.
Journal of digital imaging
Oct 1, 2019
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review o...
Comprehensive Word-Level Classification of Screening Mammography Reports Using a Neural Network Sequence Labeling Approach.
Journal of digital imaging
Oct 1, 2019
Radiology reports contain a large amount of potentially valuable unstructured data. Recently, neural networks have been employed to perform classification of radiology reports over a few classes at the document level. The success of neural networks i...
Optical Coherence Tomography Vulnerable Plaque Segmentation Based on Deep Residual U-Net.
Reviews in cardiovascular medicine
Sep 30, 2019
Automatic and accurate segmentation of intravascular optical coherence tomography imagery is of great importance in computer-aided diagnosis and in treatment of cardiovascular diseases. However, this task has not been well addressed for two reasons. ...
Radiomics: Data Are Also Images.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Sep 1, 2019
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated onc...
State-of-the-Art Deep Learning in Cardiovascular Image Analysis.
JACC. Cardiovascular imaging
Aug 1, 2019
Cardiovascular imaging is going to change substantially in the next decade, fueled by the deep learning revolution. For medical professionals, it is important to keep track of these developments to ensure that deep learning can have meaningful impact...
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.
Journal of digital imaging
Aug 1, 2019
Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natu...
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.
Journal of digital imaging
Aug 1, 2019
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...
U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instruments from Laparoscopic Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
With the advent of robot-assisted surgery, there has been a paradigm shift in medical technology for minimally invasive surgery. However, it is very challenging to track the position of the surgical instruments in a surgical scene, and accurate detec...
Conditional Adversarial Transfer for Glaucoma Diagnosis.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Deep learning has achieved great success in image classification task when given sufficient labeled training images. However, in fundus image based glaucoma diagnosis, we often have very limited training data due to expensive cost in data labeling. M...
Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI.
Abdominal radiology (New York)
Jun 1, 2019
PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without t...
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