A Deep Learning Approach to Structured Signal Recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach

Functional outcome of patients undergoing lumbar discectomy

Background: Sciatica resulting from a lumbar intervertebral disc herniation is the most common cause of radicular leg pain in adult working population. It can be treated with both conservative and operative methods. In our study, surgical treatment of lumbar disc prolapse has been done by open discectomy. We wish to assess the outcome of surgery in patients with lumbar disc prolapse undergoing lumbar discectomy.Methods: 40 patients were included in this study and were followed up for up to 1 year postoperatively. We assessed the outcome of each patient with ODI and VAS post-operatively and on follow-up at 3 weeks, 6 months and 1 year. Subjective evaluation of the patient’s satisfaction at the final follow-up was also done.Results: We found that males had higher incidence of PIVD with an average duration of symptoms before surgery about 8.62 months. Left side was most involved and level l4-l5 was most involved level. The mean ODI and VAS score pre-operatively were 26.85±4.20 and 7.73±0.88 respectively, which changed to 4.48±5.15 and 1.70±1.57, respectively at 1 year post-operative follow-up. These were statistically highly significant. Most of the patients (34) gave a subjective evaluation as excellent at 1 year follow-up.Conclusions: Our study established that open discectomy has a satisfactory functional outcome and leads to a significant improvement in the patients’ quality of life

Use of dorsalis pedis artery flap in coverage of distal lower leg defects

Soft tissue defect in the distal one third of leg have always posed a challenge for reconstructive surgeons. Such wounds are difficult to manage due the tenuous blood supply, limited subcutaneous cover over the tendons and bones. The aim of our study is to investigate the outcome of Dorsalis pedis artery flap for the coverage of such defects. In the present study, we share our clinical experience with the use of dorsalis pedis artery flap for the coverage of defect in the distal one third leg. This is a series of 4 cases where dorsalis pedis artery flap was used to cover lower one third defect. One case had focal squamous cell carcinoma due to long standing post burns contracture in distal one third of leg anteriorly. Other 3 cases had chronic non healing ulcer in the malleolar region. Patient outcome was assessed according to patients’ age distribution, duration of surgery, hospital stay, and post-operative complications. All 4 patients had excellent outcome with no major donor site complications, infection, and graft loss. Donor site was closed with split thickness skin graft. One patient developed a minor raw area over the dorsum of foot which healed secondarily. Although a potential risk in applying this flap is insufficient venous drainage, no problems with blood inflow or outflow were encountered in the present case series. The flaps survived, and the patient had good postoperative outcome. Hence dorsalis pedis flap can be used for the coverage of the distal foot as a good option

Medial plantar artery flap: a versatile workhorse flap for foot reconstruction, our experience

Soft tissue defect in the foot is commonly seen as it is more prone to trophic ulcers since it is the main weight bearing area of the body. Reconstruction of the weight bearing area of the foot requires the provision of a stable, supple, durable and preferably sensate skin coverage. Following Sir Gilli’s principle of replacing like with like, medial plantar artery flap provides an anatomically similar, glabrous skin for coverage on the plantar surface. In the present study, we share our clinical experience with the use of medial plantar artery flap for coverage of soft tissue defect over sole of foot. At our institution, a total of 10 patients presented with soft tissue defect of the sole, underwent medial plantar artery flap coverage. All the 10 patients were diagnosed cases of type 2 DM. patient outcome was assessed according to patients’ age distribution, duration of surgery, hospital stay, and post operative complications. Out of all the 10 patients, 5 were male and 5 were female. All the flaps healed uneventfully without major complications like partial flap necrosis. Donor site was covered with split thickness skin graft. There was suture site dehience in 2 cases which healed with secondary healing. Medial plantar artery flap has been described as an optimal reconstructive option for this type of soft tissue defect.

An identity crisis: the need for core competencies in undergraduate medical education

A medical student perspective on the role of core competencies in undergraduate medical education in light of medical education reform associated with recent Flexner II

StyleGAN2-based Out-of-Distribution Detection for Medical Imaging

One barrier to the clinical deployment of deep learning-based models is the presence of images at runtime that lie far outside the training distribution of a given model. We aim to detect these out-of-distribution (OOD) images with a generative adversarial network (GAN). Our training dataset was comprised of 3,234 liver-containing computed tomography (CT) scans from 456 patients. Our OOD test data consisted of CT images of the brain, head and neck, lung, cervix, and abnormal livers. A StyleGAN2-ADA architecture was employed to model the training distribution. Images were reconstructed using backpropagation. Reconstructions were evaluated using the Wasserstein distance, mean squared error, and the structural similarity index measure. OOD detection was evaluated with the area under the receiver operating characteristic curve (AUROC). Our paradigm distinguished between liver and non-liver CT with greater than 90% AUROC. It was also completely unable to reconstruct liver artifacts, such as needles and ascites.Comment: Extended abstract published in the "Medical Imaging Meets NeurIPS" workshop at NeurIPS 2022. Original abstract can be found at http://www.cse.cuhk.edu.hk/~qdou/public/medneurips2022/125.pd

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Despite new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark.Comment: 22 pages, NeurIPS 202