Search Results for author:
Found 13 papers, 7 papers with code
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans
In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost.
Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets
To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression
We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence.
Transformer Lesion Tracker
Previous methods typically lack the integration of local and global information.
Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems.
An attention-based multi-resolution model for prostate whole slide imageclassification and localization
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics.
Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models
This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
