Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

(Bin Li presenting on June 30 at 1:00 PM and 7:00 PM Eastern Time) 

Whole slide imaging is one of the essential tools in modern histopathology and presents a great opportunity for machine learning applications. Whole slide images (WSIs) have very high resolutions and usually lack localized patch-level annotations. Consequently, the classification of WSIs is often cast as a multiple instance learning (MIL) problem, where a patch-based classifier is trained using slide-level labels. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. We demonstrate that self-supervised contrastive learning can produce robust representations for MIL without the need for patch-level labels, alleviating the issue of prohibitive memory cost for training deep models. Our model offers significant improvement in classification accuracy for both unbalanced and balanced WSI datasets and under both binary and multiple class MIL settings. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems. Collaborating with hardware and computer vision groups, we demonstrate the use of WSI classification as a driving use case for developing deep learning accelerators and efficient training paradigms for related problems such as video and language analysis.

Efficient and Secure Learning across Memory Hierarchy

(Saransh Gupta, UC San Diego, presenting on Wednesday, June 2, 2021 at 1:00 & 7:00 PM ET)

Recent years have witnessed a rapid growth in the amount of generated data. Learning algorithms, like hyperdimensional (HD) computing, promise to reduce the computation complexity of processing such a huge amount of data. However, traditional computing systems are highly inefficient for such algorithms, mainly due to the limited cache capacity and memory bandwidth. In this talk, we propose a processing in-memory (PIM)-based HD computing architecture that accelerates all phases of the HD computing pipeline namely, encoding, training, retraining, and inference. Our architecture is enabled by fast and energy-efficient in-memory logic operations, combined with a hardware-friendly distance metric. Our proposed PIM solution provides 434x speedup as compared to the state-of-the-art HD computing implementations.

While this makes learning less reliant on the cloud, many applications, most notably in healthcare, finance, and defense, still need cloud computing and demand privacy which today’s solutions cannot fully provide. Fully homomorphic encryption (FHE) elevates the bar of today’s solutions by adding confidentiality of data during processing, while introducing significant data size expansion. In this talk, we also present a design of the first PIM-based accelerators of both client and server using the latest Ring-GSW based fully homomorphic encryption schemes. Our design supports various security levels and provides on average 2007x higher throughput than the best implementation while running FHE-enabled neural networks.