Professor Dr Kartini Rahmat
Associate Professor Dr Jeannie Wong Hsiu Ding
Profssor Dr See Mee Hoong
Dr Teoh Kean Hooi
Professor Dr Nur Aihah Binti Mohd Taib
Professor Dr Norlisah binti Mohd Ramli
Associate Professor Dr Mohammad Nazri bin Md. Shah
Dr Caroline Judy Westerhout
Dr Chan Wai Yee
Professor Dr Ng Kwan Hoong
Professor Dr Chan Chee Seng
Dr Tan Li Kuo
Associate Professor Farhana Binti Fadzli
Dr Ng Wei Lin
Professor Dr Anushya A/P Vijayananthan
Associate Professor Dr Nazimah Ab Mumin
Associate Professor Dr Marlina Tanty Ramli Hamid
1Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya
2Department of Surgery, Faculty of Medicine, Universiti Malaya
3Department of Pathology, Faculty of Medicine, Universiti Malaya
4Faculty of Computer Science and Information Technology, Universiti Malaya
5Department of Radiology, Faculty of Medicine, Universiti Teknology MARA
Breast cancer is the most common cancer in women globally, with mammography being the primary imaging modality. However, its sensitivity decreases in women with dense breasts, particularly among Asian women. Ultrasound and MRI aid in lesion characterisation. Computer-aided diagnosis (CAD) and AI-based systems enhance diagnostic efficiency. Radiomics, which extracts quantitative features from medical images, is employed for tumour phenotyping. BREOMICS aims to leverage multi-modality radiomics, especially in dense breasts. We developed a semi-supervised GAN to augment training datasets for deep learning radiomics (DLR), achieving promising results in classifying breast lesions. Additionally, Radioport, using "text attention," improves radiologists' understanding of deep learning results. We also proposed to develop a nomogram to combine radiological and radiomics features in breast cancer classification.
• Ab Mumin, N., et al., Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review. Acad Radiol, 2022. 29 Suppl 1: p. S89-s106.
• Pang, T., et al., Deep learning radiomics in breast cancer with different modalities: Overview and future. Expert Systems with Applications, 2020. 158: p. 113501.
• Pang, T., et al., Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification. Comput Methods Programs Biomed, 2021. 203: p. 106018.
• Letchumanan, N., et al., A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images. J Digit Imaging, 2023. 36(4): p. 1533-1540.
• Hamyoon, H., et al., Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts. European journal of radiology, 2022. 157: p. 110591.
• Pang, T., et al., Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification. Phys Med Biol, 2024. 69(6).