Collaborating Authors


AI Widens Search Spaces and Promises More Hits in Drug Discovery


Traditional drug discovery techniques are all about brute force--and a little bit of luck. Basically, large-scale, high-throughput screening is used to cover a search space. The process is a little like conducting antisubmarine warfare without the benefit of sonar. Unsurprisingly, very few of the depth charges (drug candidates) hit their targets and achieve the desired results (successful clinical trials). The seas are simply too vast.

Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study Artificial Intelligence

Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compare the performance of eight state-of-the-art detection methods, including Transformer-based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline is designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalizes better than state-of-the-art transfer learning-based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis is performed to identify the covariate shifts with bigger effects on the detection performance, such as due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning-based breast cancer detection.

Seeing into the future: Personalized cancer screening with artificial intelligence


While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those who don't get mammograms. Meanwhile, others argue about costly and potentially traumatic false positives: A meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography. Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch-all: Women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind.

Personalized cancer screening with AI


While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist: advocates argue for the ability to save lives, (women aged 60 to 69 had a 33 percent lower risk of dying compared to those who didn't get mammograms), and another camp argues about costly and potentially traumatic false positives (a meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography). Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch all: women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening? Out of this came Tempo, a technology for creating risk-based screening guidelines.

Artificial intelligence in clinical research of cancers


As a result, AI excels at handling large volumes and complex data, and identifying characteristic from the data, which the human brain cannot recognize. Although AI has been rapidly incorporated into oncologic research, the development of AI solutions is still in its infancy. Only a few AI-based applications have been approved for use in practice, e.g.

Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review Artificial Intelligence

Background: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Methods: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.

Identifying Women with Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms Artificial Intelligence

Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNN_{Fused} to that of trained only on real RCDT images, CNN_{Real}, and to that of trained only on simulated RCDT images, CNN_{Simulated}. The test AUC for CNN_{Fused} was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was statistically better (p-value < 0.02) than the CNN_{Real} AUC of 0.70 with a 95CI of [0.64, 0.77] and CNN_{Simulated} AUC of 0.68 with a 95CI of [0.62, 0.75]. It showed that CGAN simulated mammograms can help MO cancer detection.

Could Artificial Intelligence Diagnose Skin Cancer?


Artificial intelligence sounds futuristic, but it's already all around us (yes, talking about you, Alexa). It's also a burgeoning area in health care--so what does that mean for your life? Here's an example of how AI works to make time-consuming tasks simpler for people: In radiology, physicians are trained to analyze x-rays, CT scans, and other images for abnormalities; this requires individual study of hundreds of thousands of images to become familiar with what's normal and what's not. Computers can now be used to translate an image into data, compare that data against a larger data set comprised of both normal and abnormal images, and produce a quantitative assessment of potential abnormalities. Recent studies have shown that some AI algorithms perform as well as radiologists in analyzing mammograms for breast cancer, and when used by radiologists as an aid, they can enhance diagnostic accuracy. In dermatology, another specialty that relies on image recognition, there is similar enthusiasm for the use of AI in diagnosing serious skin conditions, including cancer.

A Scalable AI Approach for Clinical Trial Cohort Optimization Artificial Intelligence

FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.

Artificial Intelligence Aids in Discovery of New Prognostic Biomarkers for Breast Cancer


Scientists at Case Western Reserve University have used artificial intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment -- and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role -- using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.