Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization

Recently, deep learning-based methods have dominated image dehazing domain. Although very competitive dehazing performance has been achieved with sophisticated models, effective solutions for extracting useful features are still under-explored. In addition, non-local network, which has made a breakthrough in many vision tasks, has not been appropriately applied to image dehazing. Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper. We start with extracting richer features for dehazing. Specifically, we design a multi-stream feature extraction (MSFE) sub-block, which contains three parallel convolutions with different receptive fields (i.e., $1\times 1$, $3\times 3$, $5\times 5$) for extracting multi-scale features. Following MSFE, we employ an attention sub-block to make the model adaptively focus on important channels/regions. The MSFE and attention sub-blocks constitute our MSFAB. Then, we design a cross non-local block (CNLB), which can capture long-range dependencies beyond the query. Instead of the same input source of query branch, the key and value branches are enhanced by fusing more preceding features. CNLB is computation-friendly by leveraging a spatial pyramid down-sampling (SPDS) strategy to reduce the computation and memory consumption without sacrificing the performance. Last but not least, a novel detail-focused contrastive regularization (DFCR) is presented by emphasizing the low-level details and ignoring the high-level semantic information in the representation space. Comprehensive experimental results demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters.Comment: submitted to IEEE TCYB for possible publicatio

Prompt-based test-time real image dehazing: a novel pipeline

Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (e.g., CycleGAN, prior loss). However, most of them need very complicated training procedures to achieve satisfactory results. In this work, we present a totally novel testing pipeline called Prompt-based Test-Time Dehazing (PTTD) to help generate visually pleasing results of real-captured hazy images during the inference phase. We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i.e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing. Accordingly, we first apply a prompt generation module (PGM) to generate a visual prompt, which is the source of appropriate statistical perturbations for mean and standard deviation. And then, we employ the feature adaptation module (FAM) into the existing dehazing models for adjusting the original statistics with the guidance of the generated prompt. Note that, PTTD is model-agnostic and can be equipped with various state-of-the-art dehazing models trained on synthetic hazy-clean pairs. Extensive experimental results demonstrate that our PTTD is flexible meanwhile achieves superior performance against state-of-the-art dehazing methods in real-world scenarios. The source code of our PTTD will be made available at https://github.com/cecret3350/PTTD-Dehazing.Comment: update github link (https://github.com/cecret3350/PTTD-Dehazing

Broadcasting Quantum Fisher Information

It is well known that classical information can be cloned, but non-orthogonal quantum states cannot be cloned, and non-commuting quantum states cannot be broadcast. We conceive a scenario in which the object we want to broadcast is the statistical distinguishability, as quantified by quantum Fisher information, about a signal parameter encoded in quantum states. We show that quantum Fisher information cannot be cloned, whilst it might be broadcast even when the input states are non-commuting. This situation interpolates between cloning of classical information and no-broadcasting of quantum information, and indicates a hybrid way of information broadcasting which is of particular significance from both practical and theoretical perspectives.Comment: 5 pages. Improved version. Any comments is welcom

You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray

Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods attempted this task in either fully supervised settings which demanded costly mass per-abnormality annotations, or weakly supervised settings which still lagged badly behind fully supervised methods in performance. In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa. Concretely, based on the classical teacher-student pseudo label distillation (TSD) paradigm, we additionally introduce an auxiliary report classification model, whose prediction is used for report-guided pseudo detection label refinement (RPDLR) in the primary vision detection task. Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively. To this end, we effectively incorporate the weak supervision by reports into the semi-supervised TSD pipeline. Besides the cross-modal pseudo label refinement, we further propose an intra-image-modal self-adaptive non-maximum suppression, where the pseudo detection labels generated by the teacher vision model are dynamically rectified by high-confidence predictions by the student. Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods

Therapeutic Electromagnetic Field (TEMF) and gamma irradiation on human breast cancer xenograft growth, angiogenesis and metastasis

BACKGROUND: The effects of a rectified semi-sinewave signal (15 mT amplitude, 120 pulses per second, EMF Therapeutics, Inc.) (TEMF) alone and in combination with gamma irradiation (IR) therapy in nude mice bearing a human MDA MB231 breast cancer xenograft were tested. Green fluorescence protein transfected cancer cells were injected into the mammary fat pad of young female mice. Six weeks later, mice were randomly divided into four treatment groups: untreated controls; 10 minute daily TEMF; 200 cGy of IR every other day (total 800 cGy); IR plus daily TEMF. Some mice in each group were euthanized 24 hours after the end of IR. TEMF treatment continued for 3 additional weeks. Tumor sections were stained for: endothelial cells with CD31 and PAS or hypoxia inducible factor 1α (HIF). RESULTS: Most tumors <35 mm(3 )were white but tumors >35 mm(3 )were pink and had a vascularized capsule. The cortex within 100 microns of the capsule had little vascularization. Blood vessels, capillaries, and endothelial pseudopods were found at >100 microns from the capsule (subcortex). Tumors >35 mm(3 )treated with IR 24 hours previously or with TEMF had decreased blood vessels in the subcortex and more endothelial pseudopods projecting into hypoxic, HIF positive areas than tumors from the control group. Mice that received either IR or TEMF had significantly fewer lung metastatic sites and slower tumor growth than did untreated mice. No harmful side effects were attributed to TEMF. CONCLUSION: TEMF therapy provided a safe means for retarding tumor vascularization, growth and metastasis

Therapeutic evolution in HR+/HER2- breast cancer: from targeted therapy to endocrine therapy

Breast cancer, a complex and varied disease, has four distinct subtypes based on estrogen receptor and human epidermal growth factor receptor 2 (HER2) levels, among which a significant subtype known as HR+/HER2-breast cancer that has spurred numerous research. The prevalence of breast cancer and breast cancer-related death are the most serious threats to women’s health worldwide. Current progress in treatment strategies for HR+/HER2-breast cancer encompasses targeted therapy, endocrine therapy, genomic immunotherapy, and supplementing traditional methods like surgical resection and radiotherapy. This review article summarizes the current epidemiology of HR+/HER2-breast cancer, introduces the classification of HR+/HER2-breast cancer and the commonly used treatment methods. The mechanisms of action of various drugs, including targeted therapy drugs and endocrine hormone therapy drugs, and their potential synergistic effects are deeply discussed. In addition, clinical trials of these drugs that have been completed or are still in progress are included

Doxorubicin in Combination with a Small TGFβ Inhibitor: A Potential Novel Therapy for Metastatic Breast Cancer in Mouse Models

Recent studies suggested that induction of epithelial-mesenchymal transition (EMT) might confer both metastatic and self-renewal properties to breast tumor cells resulting in drug resistance and tumor recurrence. TGFbeta is a potent inducer of EMT and has been shown to promote tumor progression in various breast cancer cell and animal models.We report that chemotherapeutic drug doxorubicin activates TGFbeta signaling in human and murine breast cancer cells. Doxorubicin induced EMT, promoted invasion and enhanced generation of cells with stem cell phenotype in murine 4T1 breast cancer cells in vitro, which were significantly inhibited by a TGFbeta type I receptor kinase inhibitor (TbetaRI-KI). We investigated the potential synergistic anti-tumor activity of TbetaR1-KI in combination with doxorubicin in animal models of metastatic breast cancer. Combination of Doxorubicin and TbetaRI-KI enhanced the efficacy of doxorubicin in reducing tumor growth and lung metastasis in the 4T1 orthotopic xenograft model in comparison to single treatments. Doxorubicin treatment alone enhanced metastasis to lung in the human breast cancer MDA-MB-231 orthotopic xenograft model and metastasis to bone in the 4T1 orthotopic xenograft model, which was significantly blocked when TbetaR1-KI was administered in combination with doxorubicin.These observations suggest that the adverse activation of TGFbeta pathway by chemotherapeutics in the cancer cells together with elevated TGFbeta levels in tumor microenvironment may lead to EMT and generation of cancer stem cells resulting in the resistance to the chemotherapy. Our results indicate that the combination treatment of doxorubicin with a TGFbeta inhibitor has the potential to reduce the dose and consequently the toxic side-effects of doxorubicin, and improve its efficacy in the inhibition of breast cancer growth and metastasis