CAFCW24

The problem of identifying regions of interest in ultrasound images using deep learning remains a challenge, particularly in biomedical segmentation. High-quality size-annotated data are necessary for superior performance, yet data annotation is time-consuming and requires human expertise. Previous research has addressed unsupervised segmentation in various imaging modalities without labels. However, the unique nature of ultrasound images requires further attention and effort to develop truly effective approaches. In this work, we propose a novel technique for unsupervised anomaly segmentation in lesion detection, focusing on identifying abnormal patterns in ultrasound images without the need for labels. We apply Cluster MixUp data augmentation to overcome data constraints and utilize an unsupervised network to generate binary anomaly masks for suspected lesions. Our approach is validated on four key breast ultrasound images (BUS) datasets from diverse populations with varying imaging qualities. Our results show that our method performs effectively in unsupervised segmentation scenarios, indicating its potential for real-world application.

Abdalrahman Alblwi

University of Delaware

#segmentation #unsupervised_learning #anomaly #data_augmentation

Abhishek Shivanna

Advanced Computing for Health Sciences, Oak Ridge National Laboratory

The traditional drug discovery process is slow and expensive, taking approximately 10 years and $2-3 billion dollars per drug. In silico methodologies are becoming more accurate and efficient for screening large databases for new lead compounds and computational resources are becoming faster and cheaper. Furthermore, generative AI methods are being increasingly employed to suggest new molecules to be evaluated, expanding the space of de novo molecules to be evaluated. In oncological applications, validating the binding affinity of potential new drugs with molecular dynamics is an essential step in both efficacy and toxicity evaluation. Unfortunately, such methods are computationally intensive and time consuming, slowing the evaluation process and increasing compute costs. Our method aims to predict the binding free energy of docking poses to reduce the need for computationally expensive Molecular Dynamics (MD) simulations as part of a larger workflow. We have successfully designed a set of novel descriptors and trained a machine learning model to predict the binding free energy of docking poses, allowing for ligand relaxation in the protein pocket. In doing so, we are able to forego what have historically been multiple molecular dynamics runs, accelerating the evaluation process dramatically. For training, a set of 10,000 molecules provided data on 100 poses for each molecule, totaling nearly 1,000,000 data points. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) was computed for each pose as a measure of binding free energy. The interaction space descriptor employed 600 novel features that capture the relative positional information of the protein-ligand docking pose. We employed the ATOM Modeling Pipeline (AMPL), an open source machine learning molecular prediction pipeline, to train our model to predict the MMPBSA values. Prior to further hyperparameter optimization and descriptor innovation, our model’s performance has already achieved an R2 of 0.602 where 1 is the best possible score. This model is more than sufficient for the predictive role in the intended molecule evaluation process.

Sean Black, Vineeth Gutta <sean.black2@nih.gov>

Frederick National Laboratory for Cancer Research, University of Delaware

#ComputationalCancerResearch #Cancer #CancerResearch

The diagnosis of brain cancer, an extremely aggressive and challenging malignancy, stands to be gained substantially from advancements in diagnostic technologies. In this context, our research introduces a novel approach to brain tumor detection that leverages the latest innovations in artificial intelligence. We propose a novel framework that integrates Vision Transformers (ViTs) with Kolmogorov-Arnold Networks (KANs), resulting in a sophisticated and highly effective diagnostic tool. Vision Transformers have recently gained acclaim for their exceptional capabilities in handling complex image analysis tasks, owing to their ability to capture complex patterns and features within visual data. Meanwhile, KANs are recognized for their robustness in modeling complex, non-linear relationships, which enhances their utility in various classification tasks. By integrating these two technologies, our approach significantly advances the state of the art in brain tumor detection. The integration of parallelized KANs with Vision Transformers introduces a new level of performance and accuracy, better conventional neural network architectures. Our approach classifies brain tumors into three categories—gliomas, meningiomas, and pituitary tumors—using preprocessed MRI images. The training process is uniquely designed: all layers of the vision transformer model are frozen except for the final layer, which is replaced with a KAN. This modification enhances the model's classification accuracy by leveraging the advanced capabilities of KAN. It utilizes univariate functions parameterized as links instead of traditional linear weights, and features learnable activation functions, making them particularly effective for nuanced classification tasks. To meet the computational demands of processing high-resolution medical images, we employ a parallelized framework. This distributed architecture harnesses technologies such as Apache Spark, Databricks, and Nvidia GPUs, significantly improving both training and inference times. By distributing the workload across multiple nodes and GPUs, our system addresses the practical limitations of implementing AI models in healthcare, ensuring timely and accurate results are feasible. The integration of KANs with vision transformers, coupled with a parallelized infrastructure, offers a promising solution for enhancing tumor detection accuracy and efficiency. KANs' ability to model complex, non-linear relationships complements the vision transformer's strength in capturing long-range dependencies and global context. This combination pushes the boundaries of medical image analysis, potentially leading to earlier detection, better treatment planning, and improved patient outcomes. Furthermore, our approach addresses key limitations of traditional convolutional neural networks (CNNs) by leveraging the strengths of vision transformers in capturing diverse tumor presentations and KANs in refining classification precision. The parallelization of our approach opens new avenues for large-scale medical image analysis. As healthcare institutions increasingly generate vast volumes of imaging data, the need for efficient processing becomes critical. Our proposed approach accelerates both training and inference, enabling the analysis of larger and more diverse datasets. This capability leads to the development of more robust and generalizable models. Enhanced efficiency also supports more frequent updates and retraining, allowing the system to rapidly adapt to new data and evolving medical knowledge. As research advances, this approach could serve as a foundation for developing more sophisticated AI systems in medical diagnostics, potentially extending beyond brain tumor detection to other areas of healthcare.

Vijayalakshmi Saravanan

University of Texas at Tyler, Brookhaven National Laboratory, University of Texas at Dallas, Argonne National Laboratory

The rapid evolution of machine learning has led to a proliferation of sophisticated models for predicting therapeutic responses in cancer. While many of these show promise in research, standards for clinical evaluation and adoption are lacking. Here, we propose seven hallmarks by which predictive oncology models can be assessed and compared. These are Data Relevance, Expressive Architecture, Standardized Benchmarking, Generalizability, Interpretability, Accessibility, and Fairness. Considerations for each hallmark are discussed along with an example model scorecard. We encourage the broader community– including researchers, clinicians and regulators– to engage in shaping these guidelines towards a concise set of standards.

Akshat Singhal

University of California, San Diego, La Jolla, CA, USA; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Simon Fraser University, Burnaby, BC, Canada; Argonne National Laboratory, Lemont, IL, USA; Frederick National Laboratory for Cancer Research, Frederick, MD, USA; The Hastings Center, Garrison, NY, USA; City of Hope National Medical Center, Monrovia, CA, USA