Search Results for author: Ashnil Kumar

Found 13 papers, 2 papers with code

Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning

no code implementations23 Apr 2021 Yige Peng, Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim

Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality.

Management STS

Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification

no code implementations7 Jun 2019 Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim

Hence, we propose a new unsupervised feature learning method that learns feature representations to then differentiate dissimilar medical images using an ensemble of different convolutional neural networks (CNNs) and K-means clustering.

Clustering General Classification +2

Unsupervised Deep Transfer Feature Learning for Medical Image Classification

no code implementations15 Mar 2019 Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data.

General Classification Image Classification +1

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer

1 code implementation5 Oct 2018 Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim

Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis.

Tumor Segmentation

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

no code implementations16 Jul 2018 Euijoon Ahn, Jinman Kim, Ashnil Kumar, Michael Fulham, Dagan Feng

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems.

Medical Image Retrieval Retrieval

An unsupervised long short-term memory neural network for event detection in cell videos

no code implementations7 Sep 2017 Ha Tran Hong Phan, Ashnil Kumar, David Feng, Michael Fulham, Jinman Kim

We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method.

Event Detection

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

2 code implementations31 Jul 2017 Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems.

Computed Tomography (CT) Image Generation

Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

no code implementations10 Apr 2017 Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng

Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented.

Lesion Detection Segmentation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.