Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. Manual examination is highly time-consuming and prone to errors, calling for computer-aided detection, both to improve detection efficiency and reduce misdiagnosis. Over the years, a large number of such systems have been proposed, which mostly followed a two-phase paradigm with: 1) candidate detection and 2) false positive reduction. Recently, deep learning has become a dominant force in algorithm development. As for candidate detection, prior state-of-the-art was mainly based on the two-stage Faster R-CNN framework, which starts with an initial sub-net to generate a set of classagnostic region proposals, followed by a second sub-net to perform classification and bounding-box regression. In contrast, we abandon the conventional two-phase paradigm and two-stage framework altogether, and propose to train a single network to achieve end-to-end nodule detection instead, without transfer learning or further post-processing. Our feature learning model is a modification of the ResNet and feature pyramid network combined, powered by RReLU activation. The major challenge is the condition of extreme inter-class and intra-class sample imbalance, where positive samples are overwhelmed by a vast negative pool largely composed of easily discriminative samples and a handful of hard samples.Direct training on all samples can seriously undermine training efficacy. We propose a patch-based sampling strategy over a set of regularly updating anchors, which is able to narrow sampling scope to all positives and only hard negatives, effectively addressing this issue. As a result, our approach substantially outperforms prior art in terms of both accuracy and speed. Finally, the prevailing evaluation method is a FROC analysis over [1/8, 1/4, 1/2, 1, 2, 4, 8] false positives per scan, which is far from ideal in real clinical environments. Regarding practical considerations, we suggest FROC over [1,2,4] false positives as a better metric.

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Abstract. Intensive international efforts are underway toward phenotyping the entire mouse genome by modifying all its ≈25;000 genes one-by-one for comparative studies. A workload of this scale has triggered numerous studies harnessing image informatics for the identification of morphological defects. However, existing work in this line primarily rests on abnormality detection via structural volumetrics between wild-type and gene-modified mice, which generally fails when the pathology involves no severe volume changes, such as ventricular septal defects (VSDs) in the heart. Furthermore, in embryo cardiac phenotyping, the lack of relevant work in embryonic heart segmentation, the limited availability of public atlases, and the general requirement of manual labor for the actual phenotype classification after abnormality detection, along with other limitations, have collectively restricted existing practices from meeting the high-throughput demands. This study proposes, to the best of our knowledge, the first fully automatic VSD classification framework in mouse embryo imaging. Our approach leverages a combination of atlas-based segmentation and snake evolution techniques to derive the segmentation of heart ventricles, where VSD classification is achieved by checking whether the left and right ventricles border or overlap with each other. A pilot study has validated our approach at a proof-of-concept level and achieved a classification accuracy of 100% through a series of empirical experiments on a database of 15 images.