no code implementations • 17 Aug 2021 • Chi-Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, YuTing Huang, Yu-Chao Wang, Chien-Wei Peng, YouBao Tang, Wei-Chen Lee, Ta-Sen Yeh, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison
We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).
Recently, extensive research efforts have been dedicated to understanding the emotions of images.
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation.
We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network.
To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an image-to-image translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation.
Ranked #1 on Lung Nodule Segmentation on NIH
To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.
The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.
This paper proposes a novel saliency detection method by developing a deeply-supervised recurrent convolutional neural network (DSRCNN), which performs a full image-to-image saliency prediction.
This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD).