Ruchika Verma

Gabriele Campanella, Shengjia Chen, Ruchika Verma et al.

The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from two medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training new foundation models and selecting appropriate pretrained models.

Michael E. Todhunter, Sheikh Jubair, Ruchika Verma et al.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time- and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

Marwa Ismail, Ramon Correa, Kaustav Bera et al.

With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches fall into 2 categories: (1) deep-learning/radiomics (context-based), using image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging has been the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) "virtual biopsy" maps that incorporate context-features from co-localized biopsy site along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel probability of the presence of a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model to improve the voxel-wise prediction probability. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from corresponding biopsy, to predict the mutation status of 2 driver genes in Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFR amplification status,compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT status, training and testing accuracies using SpACe were 88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and 79.3% and 68.4% using DL. Following validation,SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.