Implementation of Spatial LDA uses variational inference for learning model parameters and unfortunately does not scale well with dataset size and does not lend itself to speed-up via GPUs / TPUs. As researchers begin to collect larger in-situ multiplexed imaging datasets, there is a growing need for more scalable approaches for analysis of microenvironments. Here we propose a VAE-style network which we call Neural Spatial LDA extending the auto-encoding Variational Bayes formulation of classical LDA. We show Neural Spatial LDA achives significant speed-up over Spatial LDA while at the same time recovering similar topic distributions thus enabling its use in large data domains.

We present a pathology deep learning system (PDLS) that performs hierarchical classification of digitized whole slide image (WSI) specimens into six classes defined by their morphological characteristics, including classification of “Melanocytic Suspect” specimens likely representing melanoma or severe dysplastic nevi . We trained the system on 7,685 images from a single lab (the reference lab), including the the largest set of triple-concordant melanocytic specimens compiled to date, and tested the system on 5,099 images from two distinct validation labs. We achieved Area Underneath the ROC Curve (AUC) values of 0.93 classifying Melanocytic Suspect specimens on the reference lab, 0.95 on the first validation lab, and 0.82 on the second validation lab.

Higgins et al. introduce β-VAEs, which penalizes the KL divergence between the variational posterior and the prior using the hyperparameter β. Bottleneck-VAEs increase the capacity of the information bottleneck as the training progresses thus offering better reconstructions than β-VAEs. In this work, we show that Bottleneck-VAEs and β-VAEs are closely connected and propose a decreasing schedule for the hyperparameter β in β-VAEs that controls the information capacity similar to the hyperparameter C in the objective function of Bottleneck-VAEs

We present and validate a deep learning system which classifes digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 diferent vendors. The system’s use of deep-learning-based confdence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting

In this paper, we employ a simple regularizer on the number of hidden units in the networks, which we refer to as adaptive network regularization (ANR). This method places a penalty on the number of hidden units per layer, designed to encourage compactness and flexibility of the network architecture. This penalty acts as the sole tuning parameter over the network size, increasing simplicity during training. We motivate this model using small-variance asymptotics—a Bayesian neural network with a Poisson number of units per layer becomes our model in the small-variance limit