Wavelet Feature Maps Compression for Image-to-Image CNNs

Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well for classification, it may cause severe performance degradation in image-to-image tasks such as semantic segmentation and depth estimation. In this paper, we propose Wavelet Compressed Convolution (WCC) -- a novel approach for high-resolution activation maps compression integrated with point-wise convolutions, which are the main computational cost of modern architectures. To this end, we use an efficient and hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and define the convolution on the compressed activation map. We experiment on various tasks, that benefit from high-resolution input, and by combining WCC with light quantization, we achieve compression rates equivalent to 1-4bit activation quantization with relatively small and much more graceful degradation in performance

Quality assessment of fetal middle cerebral and umbilical artery Doppler images using an objective scale within an international randomized controlled trial

Objectives To determine the quality of Doppler images of the fetal middle cerebral artery (MCA) and umbilical artery (UA) using an objective scale, and to determine the reliability of this scale, within a multicenter randomized controlled trial (Revealed versus concealed criteria for placental insufficiency in unselected obstetric population in late pregnancy (Ratio37)). Methods The Ratio37 trial is an ongoing randomized, open-label, multicenter controlled study of women with a low-risk pregnancy recruited at 20 weeks. Doppler measurements of the fetal MCA and UA were performed at 37 weeks. Twenty patients from each of the six participating centers were selected randomly, with two images evaluated per patient (one each for the MCA and UA). The quality of a total of 240 images was evaluated by six experts, scored on an objective scale of six items. Inter- and intrarater reliability was assessed using the Fleiss-modified kappa statistic for ordinal scales. Results On average, 89.2% of MCA images and 85.0% of UA images were rated as being of perfect (score of 6) or almost perfect (score of 5) quality. Kappa values for intrarater reliability of quality assessment were 0.90 (95% CI, 0.88-0.92) and 0.90 (95% CI, 0.88-0.93) for the MCA and UA, respectively. The corresponding inter-rater reliability values were 0.85 (95% CI, 0.81-0.89) and 0.84 (95% CI, 0.80-0.89), respectively. Conclusion The quality of MCA and UA Doppler ultrasound images can be evaluated reliably using an objective scale. Over 85% of images, which were obtained by operators from a broad range of clinical practices within a multicenter study, were rated as being of perfect or almost perfect quality. Intra- and inter-rater reliability of quality assessment was very good