Publications (15)
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
The identification of melanoma involves an integrated analysis of skin lesion images acquired using the clinical and dermoscopy modalities. Dermoscopic images provide a detailed vi…
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a pau…
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
Euijoon Ahn, Jinman Kim, Ashnil Kumar +2
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 t…
An unsupervised long short-term memory neural network for event detection in cell videos
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events…
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation
Kai-Chieh Liang, Lei Bi, Ashnil Kumar +2
Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) scans are regarded as the imaging modality of choice for the assessment of treatment response…
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks
Lei Bi, Jinman Kim, Ashnil Kumar +1
Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmenta…
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PE…
Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning
Yige Peng, Lei Bi, Ashnil Kumar +3
Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue…
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT
Xiaohang Fu, Lei Bi, Ashnil Kumar +2
Accurate characterisation of visual attributes such as spiculation, lobulation, and calcification of lung nodules is critical in cancer management. The characterisation of these at…
Semi-supervised estimation of event temporal length for cell event detection
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of…
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
Medical image analysis using supervised deep learning methods remains problematic because of the reliance of deep learning methods on large amounts of labelled training data. Altho…
Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context
Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar +5
Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone. The most direct opportun…
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Lei Bi, Jinman Kim, Ashnil Kumar +2
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing ima…
Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
Ashnil Kumar, Michael Fulham, Dagan Feng +1
The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of…
High-Dimensional Data Visualization by Interactive Construction of Low-Dimensional Parallel Coordinate Plots
Takayuki Itoh, Ashnil Kumar, Karsten Klein +1
Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the repr…