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Research in healthcare systems has been mainly focused on automating several tasks in the clinical pipeline, aiming at enhancing or expediting physician’s diagnosis. Prostate Cancer (PCa) is widely known as one of the most frequently occurring cancer in diagnosis in men. If early diagnosed, the mortality rate is almost zero. Yet, should it goes under the radar -in the metastasis stage- that rate plummets at 31% [1]. For diagnosis, after a high level of Prostatic Specific Antigen (PSA), patients are recommended to undergo an MRI screening. Those patients with suspiciously looking lesions on the prostate gland will eventually undergo biopsy. The histology gives the definite answer whether a patient suffers from cancer. Nevertheless, it is observed in real practice that urologists tend to over-diagnose patients with csPCa, thus increasing the number of unnecessary biopsies. As such, this increases the diagnostic costs and patient discomfort.

Computer vision empowered with AI has shown promising results in the last decade. Computer-Aided Diagnosis (CAD) tools benefit from AI’s rapid evolution and many works have been proposed to automatically perform lesion detection and segmentation. Even though the Deep Learning (DL) paradigm is ubiquitous in modern AI, medical applications require more transparency behind feature extraction and thereby DL is often deemed as a “black-box” from physicians. Our proposed pipeline, PCa-RadHop [2], employs a novel and linear module for data-driven feature extraction and hence the decision making becomes more interpretable. PCa-RadHop receives three different modalities as input from the MRI-scanner (i.e. T2w, ADC, DWI), pertinent to PCa diagnosis. It consists of two stages. The first stage calculates a probability map about csPCa presence in a voxel-wise manner, while the second stage is meant to reduce the false positive rate on that heatmap [...]

Professor C.-C. Jay Kuo, Director of MCL, attended the IEEE Conference on Multimedia Exposition (ICME) held in Niagara Falls, Canada, from July 15-19, 2024. Professor Kuo had dual roles in this conference as a Panel co-Chair and a keynote speaker. Professor Kuo gave his keynote on 7/18 (Thursday) on “Toward Interpretable and Sustainable AI via Green Learning.” Besides, Dr. Kuo and Dr. Zicheng Liu of AMD organized a panel as summarized below.

Panel Title: Generative AI – Opportunities, Challenges, and Open Questions

Panel Background: Generative AI has received a lot of attention due to the tremendous success of ChapGPT. Large foundation models have been trained, leading to various demos and potential applications such as text-to-image and text-to-video cross-domain generations. Resources have been invested in building massive computational and storage infrastructures. Furthermore, data collection and cleaning are essential to high system performance. In the face of these rapid developments, this panel will discuss opportunities, challenges, and open questions associated with generative AI.

Four panelists were invited:

Rogerio Feris, IBM Research

Lijuan Wang, Microsoft Research

Jiebo Luo, University of Rochester

Junsong Yuan, State University of New York at Buffalo

Q&A topics:

Today’s generative AI is tilted more toward “engineering” than “science.” Will this be a concern in the long run?

What are the major shortcomings of the current large foundation models?

How vital are “data collection and cleaning” tasks in generative AI? How do large companies carry out such tasks? Will we run out of data? If so, how soon?

Will “copyright,” “plagiarism,” and “hallucination” be issues? How can we address them? How can we trust the answers?

What roles can small AI companies and academia with limited resources play?

What is the future R&D direction of generative AI? What will be the next big breakthroughs?

Dr. Kuo also had a [...]

Magnetic resonance imaging (MRI) is a good way to detect clinically significant prostate cancer and guide biopsies, due to the superior resolution and contrast of imaging, without harming the human body. Based on prostate MRI, prostate segmentation is a process to localize prostate boundaries for radiotherapy and automate the calculation of the prostate volume. Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning [1].

It is very hard to collect and obtain large annotated datasets for AI In Healthcare. We worked with USC Keck Medical School on this project, and they provided us with a large medical dataset, which was very precious and helpful. In addition to this dataset, we also used some public datasets like ISBI-2013 [2] and PROMISE-12[3] to analyze and evaluate our Green U-Shaped Learning (GUSL) methodology.

Our Green U-Shaped Learning (GUSL) framework is a feed-forward encoder-decoder system based on successive subspace learning (SSL), and it consists of two modules: 1) encoder: fine to coarse unsupervised representation learning with cascaded VoxelHop units, and 2) decoder: coarse to fine segmentation prediction with voxel-wise regression and local error correction. Our model is lightweight and totally transparent while keeping comparable performance.

We have done 5 cross-validations for the dataset from USC Keck Medical School. For T2-cube MRIs, the Dice Similarity Coefficient (DSC) of the prostate segmentation was over 93%. The USC Keck Medical School doctors were very satisfied with these impressive results. In the next step, we will apply our GUSL model to some public datasets and then compare and analyze the performance of our method with some state-of-the-art Deep Learning methods. In the future, we aim to develop methods for segmenting other organs like cardiac. I hope our methods [...]

Professor C.-C. Jay Kuo, Director of MCL, visited Singapore from June 17-23 and attended two events in his Asia trip.For the first event, Professor Kuo gave a seminar titled “Demystify Artificial Intelligence and Technology Outlook” at the Nanyang Technological University on June 20 (Wednesday). The abstract of his talk is given below.“The term “Artificial Intelligence (AI)” was coined in 1956. Although the field evolved slowly in the first 55 years, we have witnessed rapid advances in AI in the last decade (e.g., the ChatGPT service). Questions are raised about AI’s role in human society and civilization, e.g., whether AI will replace human intelligence (HI), how HI and AI complement each other, etc. I will shed light on them. Then, I will comment on the limitations of today’s deep-learning-based (DL) AI models. DL-based models are neither interpretable nor sustainable. An alternative methodology is desired. To this end, I have investigated a new statistically based AI/ML framework called “Green Learning” (GL). GL significantly reduces the model size and complexity of DL models while yielding competitive performance and allowing mathematical transparency. GL adopts the feedforward one-pass training pipeline, so all intermediate results are explainable. Finally, I will address the AI role in human society and issues with today’s chatbots, such as bias and fairness.”Professor Kuo’s talk was hosted by Professor Woon Seng Gan at NTU. He also had a reunion with Professor Jiaying Liu of Peking University. Professor Liu was a visiting PhD student at MCL from August 2007 to August 2008.For the 2nd activity, Professor Kuo delivered a keynote speech at the Workshop titled “Emerging Trends and Innovations in Machine Learning and AI” on June 21 (Friday) organized by APSIPA Singapore Chapter and quite a few [...]

Nuclei segmentation is a fundamental task required to analyze the underlying nuclei structure of an organ of interest. Cancer starts from the cells, and understanding the nuclei shapes, sizes and distribution can provide cues on whether or not a patient has cancer. Further analysis can also help in cancer grading and prognosis. However, studying whole slide images of biopsied tissues requires a large amount of time and effort. 

Such monotonous and laborious tasks can be simplified by using AI and ML, and can perhaps improve the accuracy of the detections as well. Some challenges in the nuclei segmentation task include inherent staining variations in the WSI, a wide variety of shapes and sizes in nuclei, and irregular boundaries which make it difficult to track the actual contours. Most of the current research in this area involves deep learning based architectures like the U-Net, R-CNN, and even Vision Transformer. These methods require a large number of training samples and high complexity to achieve generalization among the variations inherent in nuclei from different organs.

We propose to use a light-weight, interpretable, and simple Green Learning based approach to perform Nuclei Segmentation. Prior work on highly effective Unsupervised Nuclei Instance Segmentation (HUNIS) [1] forms the first stage of our current approach. To further improve HUNIS results, we now focus on the regions where HUNIS requires the help of labels. We divide our current task into two stages: (i) to identify those areas where we need help and (ii) to correct those areas towards their actual class. With the help of Saab Transform, our main task now is to perform feature engineering to identify the ideal features to implement the above two stages. 

References:

[1] V. Magoulianitis, Y. Yang, and C.-C. J. [...]