DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery

Semi-supervised learning aims to help reduce the cost of the manual labelling process by leveraging valuable features extracted from a substantial pool of unlabeled data alongside a limited set of labelled data during the training phase. Since pixel-level manual labelling in large-scale remote sensing imagery is expensive, semi-supervised learning becomes an appropriate solution to this. However, most of the existing consistency learning frameworks based on network perturbation are very bulky. There is still a lack of lightweight and efficient perturbation methods to promote the diversity of features and the precision of pseudo labels during training. In order to fill this gap, we propose DiverseNet which explores multi-head and multi-model semi-supervised learning algorithms by simultaneously enhancing precision and diversity during training. The two proposed methods in the DiverseNet family, namely DiverseHead and DiverseModel, both achieve the better semantic segmentation performance in four widely utilised remote sensing imagery data sets compared to state-of-the-art semi-supervised learning methods. Meanwhile, the proposed DiverseHead architecture is simple and relatively lightweight in terms of parameter space compared to the state-of-the-art methods whilst reaching high-performance results for all the tested data sets

AMM-FuseNet: attention-based multi-modal image fusion network for land cover mapping

Land cover mapping provides spatial information on the physical properties of the Earth’s surface for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems, such as the destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping become one of the most widespread applications in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the channel attention mechanism and densely connected atrous spatial pyramid pooling (DenseASPP). In the experimental analysis, in order to verify the validity of the proposed method, we test AMM-FuseNet with three datasets whilst comparing it to the six state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state of the art, achieving less accuracy loss, even for the case with 1/20 of the training samples

Sequencing of breast cancer stem cell populations indicates a dynamic conversion between differentiation states in vivo

Peer reviewe

Concomitant Carcinoma in situ in Cystectomy Specimens Is Not Associated with Clinical Outcomes after Surgery

Objective: The aim of this study was to externally validate the prognostic value of concomitant urothelial carcinoma in situ (CIS) in radical cystectomy (RC) specimens using a large international cohort of bladder cancer patients. Methods: The records of 3,973 patients treated with RC and bilateral lymphadenectomy for urothelial carcinoma of the bladder (UCB) at nine centers worldwide were reviewed. Surgical specimens were evaluated by a genitourinary pathologist at each center. Uni- and multivariable Cox regression models addressed time to recurrence and cancer-specific mortality after RC. Results: 1,741 (43.8%) patients had concomitant CIS in their RC specimens. Concomitant CIS was more common in organ-confined UCB and was associated with lymphovascular invasion (p < 0.001). Concomitant CIS was not associated with either disease recurrence or cancer-specific death regardless of pathologic stage. The presence of concomitant CIS did not improve the predictive accuracy of standard predictors for either disease recurrence or cancer-specific death in any of the subgroups. Conclusions: We could not confirm the prognostic value of concomitant CIS in RC specimens. This, together with the discrepancy between pathologists in determining the presence of concomitant CIS at the morphologic level, limits the clinical utility of concomitant CIS in RC specimens for clinical decision-making. Copyright (C) 2011 S. Karger AG, Base

Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke.

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; &lt;4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation

De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development

Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD

Warm-Start AlphaZero Self-Play Search Enhancements

Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing results in other games is a challenge, and many researchers are looking for ways to improve results while reducing computational demands. AlphaZero's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements; it is designed to learn from scratch. We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE) and dynamically weighted combinations of these with the neural network, and Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that most of these enhancements improve the performance of their baseline player in three different (small) board games, with especially RAVE based variants playing strongly

Incidental detection of an occult oral malignancy with autofluorescence imaging: a case report

BACKGROUND: Autofluorescence imaging is used widely for diagnostic evaluation of various epithelial malignancies. Cancerous lesions display loss of autofluorescence due to malignant changes in epithelium and subepithelial stroma. Carcinoma of unknown primary site presents with lymph node or distant metastasis, for which the site of primary tumour is not detectable. We describe here the use of autofluorescence imaging for detecting a clinically innocuous appearing occult malignancy of the palate which upon pathological examination was consistent with a metastatic squamous cell carcinoma. CASE DESCRIPTION: A submucosal nodule was noted on the right posterior hard palate of a 59-year-old white female during clinical examination. Examination of this lesion using a multispectral oral cancer screening device revealed loss of autofluorescence at 405 nm illumination. An excisional biopsy of this nodule, confirmed the presence of a metastatic squamous cell carcinoma. Four years ago, this patient was diagnosed with metastatic squamous cell carcinoma of the right mid-jugular lymph node of unknown primary. She was treated with external beam irradiation and remained disease free until current presentation. CONCLUSION: This case illustrates the important role played by autofluorescence tissue imaging in diagnosing a metastatic palatal tumour that appeared clinically innocuous and otherwise would not have been biopsied