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Found 16 papers, 4 papers with code
A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening
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.
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
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).
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Recently, extensive research efforts have been dedicated to understanding the emotions of images.
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
(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.
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
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).
One Click Lesion RECIST Measurement and Segmentation on CT Scans
Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation.
E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
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.
XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation
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
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
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.
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
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.
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
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.
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD).
