DLRG schedule for autumn semester 2023
(GoogleMeet link: https://meet.google.com/wah-nwrt-pqv)

1) Sunday (Nov 5, 2023) presented by Abdel Rahman, “Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering”. doi: https://arxiv.org/pdf/2303.01903.pdf

2) Sunday (Nov 19, 2023) presented by Fatima, “ARU-GAN: U-shaped GAN based on Attention and Residual connection for super-resolution reconstruction”. doi: 10.1016/j.compbiomed.2023.107316

3) Sunday (Dec 3, 2023) presented by Israa, “Improved Arabic image captioning model using feature concatenation with pre-trained word embedding ”.  doi: 10.1007/s00521-023-08744-1

4) Sunday (Dec 17, 2023) presented by Roua’ and Sara, “Stochastic Segmentation with Conditional Categorical Diffusion Models ”.  doi: https://arxiv.org/pdf/2303.08888.pdf

5) Sunday (Dec 31, 2023) presented by Ala, “ to-be-suggested”. doi: to-be-suggested

6) Sunday (Dec 14, 2024) presented by Wasan, Maha, Mira and Mirna, “ to-be-suggested”. doi: to-be-suggested

DLRG schedule for summer semester
(GoogleMeet link: https://meet.google.com/wah-nwrt-pqv)

1) Sunday (Jul 23, 2023) presented by Fatima, “ LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis ”. doi.org/10.1016/j.neunet.2022.11.005

2) Sunday (Jul 30, 2023) presented by Abdel Rahman, “ NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs”. https://arxiv.org/pdf/2304.05866.pdf

3) Sunday (Aug 6, 2023) presented by Israa, “ Image-Text Embedding Learning via Visual and Textual Semantic Reasoning”. doi: 10.1109/TPAMI.2022.3148470

4) Sunday (Aug 13, 2023) presented by Roua’ and Sara, “ Segmentation ability map: Interpret deep features for medical image segmentation”. doi.org/10.1016/j.media.2022.102726

5) Sunday (Aug 20, 2023) presented by Bara’a, “ SECS: An Effective CNN Joint Construction Strategy for Breast Cancer Histopathological Image Classification”. doi.org/10.1016/j.jksuci.2023.01.017

6) Sunday (Sep 3, 2023) presented by Ala, “ to-be-suggested”. doi: to-be-suggested

Schedule for Spring semester
(GoogleMeet link: https://meet.google.com/wah-nwrt-pqv)

1) Sunday (Mar 12, 2023) presented by Israa, “Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering”. doi.org/10.1016/j.eswa.2022.118669

2) Sunday (Mar 26, 2023) presented by Ala, “Towards Personalized Federated Learning”. doi.org/10.1109/TNNLS.2022.3160699

3) Sunday (Apr 9, 2023) presented by Fatima : “TCGAN: a transformer-enhanced GAN for PET synthetic CT”. doi: https://doi.org/10.1364/BOE.467683

4) Sunday (Apr 30, 2023) presented by Roua’ and Sara, “A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images”. doi.org/10.1016/j.artmed.2021.102180

5) Sunday (May 7, 2023) presented by Abdel Rahman, “ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection. doi.org/10.1016/j.eswa.2022.118423.

6) Sunday (May 21, 2023) presented by Bara’a, “Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things”. doing.org/10.1109/JBHI.2023.3256974

7) Sunday (Jun 4, 2023) Haya

Web Application Security (1904710)
Semester overall marks are distributed as: midterm exam 30% (first MCQ exam 16%, second hands-on exam 14%), homework and assignments 10%, and term paper 20% (research paper 10%, oral presentation 10%).

Unpaired MR-CT Brain Image Dataset for Unsupervised Image Translation

The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).

The dataset consists of 2D image slices extracted using the RadiAnt DICOM viewer software. The extracted images are transformed to DICOM image data format with a resolution of 256×256 pixels. There are a total of 179 2D axial image slices referring to 20 patient volumes (90 MR and 89 CT 2D axial image slices). The dataset contains MR and CT brain tumour images with corresponding segmentation masks. The MR images of each patient were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to the CT images. The MR scans have a resolution of 0.7×0.6×5 mm³, while the CT scans have a resolution of 0.6×0.6×7 mm³. MR-CT Dataset can be downloaded from here.

*Please note that this data is the property of the Jordan University Hospital and is made available for download for research purposes only. Users are kindly requested to acknowledge the source of this data (https://doi.org/10.1016/j.dib.2022.108109 and implemntation in https://doi.org/10.1016/j.compbiomed.2021.104763) if used for any publication.