10 research outputs found
Investigating the performance of generative adversarial networks for prostate tissue detection and segmentation
The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively
Mammographic mass classification using filter response patches
Considering the importance of early diagnosis of breast cancer, a supervised patchâwise textonâbased approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on textureâbased classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patches from filter bank responses are utilised for generating the texton dictionary. The methodology is evaluated on the publicly available Digital Database for Screening Mammography database. Using a naive Bayes classifier, a classification accuracy of 83% with an area under the receiver operating characteristic curve of 0.89 was obtained. Experimental results demonstrated that the patchâwise textonâbased approach in conjunction with the naive Bayes classifier constructs an efficient and alternative approach for automatic mammographic mass classification
Glioma Classification Using Multimodal Radiology and Histology Data
Gliomas are brain tumours with a high mortality rate. There are various
grades and sub-types of this tumour, and the treatment procedure varies
accordingly. Clinicians and oncologists diagnose and categorise these tumours
based on visual inspection of radiology and histology data. However, this
process can be time-consuming and subjective. The computer-assisted methods can
help clinicians to make better and faster decisions. In this paper, we propose
a pipeline for automatic classification of gliomas into three sub-types:
oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and
histopathology images. The proposed approach implements distinct classification
models for radiographic and histologic modalities and combines them through an
ensemble method. The classification algorithm initially carries out tile-level
(for histology) and slice-level (for radiology) classification via a deep
learning method, then tile/slice-level latent features are combined for a
whole-slide and whole-volume sub-type prediction. The classification algorithm
was evaluated using the data set provided in the CPM-RadPath 2020 challenge.
The proposed pipeline achieved the F1-Score of 0.886, Cohen's Kappa score of
0.811 and Balance accuracy of 0.860. The ability of the proposed model for
end-to-end learning of diverse features enables it to give a comparable
prediction of glioma tumour sub-types