On Network Science and Mutual Information for Explaining Deep Neural Networks

In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.Comment: ICASSP 2020 (shorter version appeared at AAAI-19 Workshop on Network Interpretability for Deep Learning

ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks

Zero-Shot Neural Architecture Search (NAS) approaches propose novel training-free metrics called zero-shot proxies to substantially reduce the search time compared to the traditional training-based NAS. Despite the success on image classification, the effectiveness of zero-shot proxies is rarely evaluated on complex vision tasks such as semantic segmentation and object detection. Moreover, existing zero-shot proxies are shown to be biased towards certain model characteristics which restricts their broad applicability. In this paper, we empirically study the bias of state-of-the-art (SOTA) zero-shot proxy ZiCo across multiple vision tasks and observe that ZiCo is biased towards thinner and deeper networks, leading to sub-optimal architectures. To solve the problem, we propose a novel bias correction on ZiCo, called ZiCo-BC. Our extensive experiments across various vision tasks (image classification, object detection and semantic segmentation) show that our approach can successfully search for architectures with higher accuracy and significantly lower latency on Samsung Galaxy S10 devices.Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 202

Targetable ERBB2 mutation status is an independent marker of adverse prognosis in estrogen receptor positive, ERBB2 non-amplified primary lobular breast carcinoma: a retrospective in silico analysis of public datasets

© 2020 The Author(s). Background: Invasive lobular carcinoma (ILC) accounts for 10-15% of primary breast cancers and is typically estrogen receptor alpha positive (ER+) and ERBB2 non-amplified. Somatic mutations in ERBB2/3 are emerging as a tractable mechanism underlying enhanced human epidermal growth factor 2 (HER2) activity. We tested the hypothesis that therapeutically targetable ERBB2/3 mutations in primary ILC of the breast associate with poor survival outcome in large public datasets. Methods: We performed in silico comparison of ERBB2 non-amplified cases of ER+ stage I-III primary ILC (N = 279) and invasive ductal carcinoma (IDC, N = 1301) using METABRIC, TCGA, and MSK-IMPACT information. Activating mutations amenable to HER2-directed therapy with neratinib were identified using existing functional data from in vitro cell line and xenograft experiments. Multivariate analysis of 10-year overall survival (OS) with tumor size, grade, and lymph node status was performed using a Cox regression model. Differential gene expression analyses by ERBB2 mutation and amplification status was performed using weighted average differences and an in silico model of response to neratinib derived from breast cancer cell lines. Results: ILC tumors comprised 17.7% of all cases in the dataset but accounted for 47.1% of ERBB2-mutated cases. Mutations in ERBB2 were enriched in ILC vs. IDC cases (5.7%, N = 16 vs. 1.4%, N = 18, p < 0.0001) and clustered in the tyrosine kinase domain of HER2. ERBB3 mutations were not enriched in ILC (1.1%, N = 3 vs. 1.8%, N = 23; p = 0.604). Median OS for patients with ERBB2-mutant ILC tumors was 66 months vs. 211 months for ERBB2 wild-type (p = 0.0001), and 159 vs. 166 months (p = 0.733) for IDC tumors. Targetable ERBB2 mutational status was an independent prognostic marker of 10-year OS - but only in ILC (hazard ratio, HR = 3.7, 95% CI 1.2-11.0; p = 0.021). Findings were validated using a novel ERBB2 mutation gene enrichment score (HR for 10-year OS in ILC = 2.3, 95% CI 1.04-5.05; p = 0.040). Conclusions: Targetable ERBB2 mutations are enriched in primary ILC and their detection represents an actionable strategy with the potential to improve patient outcomes. Biomarker-led clinical trials of adjuvant HER-targeted therapy are warranted for patients with ERBB2-mutated primary ILC