MobileNetV2: Inverted Residuals and Linear Bottlenecks

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameter

Individualized predictions of disease progression following radiation therapy for prostate cancer.

Background: Following treatment for localized prostate cancer, men are monitored with serial PSA measurements. Refining the predictive value of post-treatment PSA determinations may add to clinical management and we have developed a model that predicts for an individual patient future PSA values and estimates the time to future clinical recurrence. Methods: Data from 934 patients treated for prostate cancer between 1987 and 2000 were used to develop a comprehensive statistical model to fit the clinical recurrence events and pattern of PSA data. A logistic regression model was used for the probability of cure, non-linear hierarchical mixed models were used for serial PSA measurements and a time-dependent proportional hazards model was used for recurrences. Data available up to February 2001 and September 2003 was used to assess the performance of the model. Results: The model suggests that T-stage, baseline PSA, and radiotherapy dosage are all associated with probability of cure. The risk of clinical recurrence in those not cured by radiotherapy is most strongly affected by the slope of the long-transformed PSA values. We show how the model can be used for individual monitoring of a patient’s disease progression. For each patient the model predicts, based upon his baseline and all post-treatment PSA values, the probability of future clinical recurrence in the validation dataset and of 406 PSA measurements obtained 1-2 years after February 2001, 92.8% were within 95% prediction limits from the model. Conclusions: This statistical model presented accurately predicts future PSA values and risk of clinical relapse. This predictive information for each individual patient, which can be updated with each additional PSA value, may prove useful to patents and physicians in determining what post-treatment salvage should be employed

Non-discriminative data or weak model? On the relative importance of data and model resolution

We explore the question of how the resolution of the input image ("input resolution") affects the performance of a neural network when compared to the resolution of the hidden layers ("internal resolution"). Adjusting these characteristics is frequently used as a hyperparameter providing a trade-off between model performance and accuracy. An intuitive interpretation is that the reduced information content in the low-resolution input causes decay in the accuracy. In this paper, we show that up to a point, the input resolution alone plays little role in the network performance, and it is the internal resolution that is the critical driver of model quality. We then build on these insights to develop novel neural network architectures that we call \emph{Isometric Neural Networks}. These models maintain a fixed internal resolution throughout their entire depth. We demonstrate that they lead to high accuracy models with low activation footprint and parameter count.Comment: ICCV 2019 Workshop on Real-World Recognition from Low-Quality Images and Video

Management preferences following radical inguinal orchidectomy for Stage I testicular seminoma in Australasia

A survey to evaluate the preferred patterns of management of Stage I seminoma was conducted during March 2001. The questionnaire was distributed by the Royal Australian and New Zealand College of Radiologists to all qualified radiation oncologists, 74 out of 170 responded. All performed a staging CT scan of the abdomen and pelvis. Thoracic imaging consisted of either chest X-ray (29%) or chest CT (38%) while 33% performed both. Fifty-four percent of radiation oncologists discussed surveillance with their patients but estimated that 5% or less would choose this option. The most commonly prescribed dose was 25 Gy in 15 or 20 fractions (79%). Sixty-five percent of respondents treated the para-aortic (PA) nodes alone. Forty-two of 48 clinicians treating the PA field reported a change in practice after publication of the Medical Research Council study in 1999. Of these, 40 and 23% perform CT scans of the pelvis annually and every 6 months. Thirty-one percent did no follow-up CT scan. Compared to a similar survey from North America, we are more likely to use PA fields and less likely to discuss surveillance. As in the USA, and in contrast to Canada, few patients choose surveillance. There is no consensus regarding the frequency of follow-up scans in either North America or Australasia.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75636/1/j.1440-1673.2002.01060.x.pd

Latino Parents\u27 Motivations for Involvement in Their Children\u27s Schooling: An Exploratory Study

This study examines the ability of a theoretical model of the parental involvement process to predict Latino parents\u27 involvement in their children\u27s schooling. A sample of Latino parents (N = 147) of grade 1 through 6 children in a large urban public school district in the southeastern United States responded to surveys assessing model-based predictors of involvement (personal psychological beliefs, contextual motivators of involvement, perceived life-context variables), as well as levels of home- and school-based involvement. Home-based involvement was predicted by partnership-focused role construction (a personal psychological belief) and by specific invitations from the student (a contextual motivator of involvement). School-based involvement was predicted by specific invitations from the teacher (a contextual motivator) and by perceptions of time and energy for involvement (a life-context variable). Results are discussed with reference to research on Latino parents\u27 involvemen