A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment. To make the labeling process less laborious and dependent on expertise, weakly-supervised semantic segmentation (WSSS) methods using class activation mapping (CAM) have been proposed. However, current CAM-based WSSS methods generate the object localization map using internal neural network information, such as gradient or trainable parameters, which can lead to suboptimal solutions. To address these issues, we propose the confidence-induced CAM (Cfd-CAM), which calculates the weight of each feature map by using the confidence of the target class. Our experiments on two brain tumor datasets show that Cfd-CAM outperforms existing state-of-the-art methods under the same level of supervision. Overall, our proposed Cfd-CAM approach improves the accuracy of brain tumor segmentation and may provide valuable insights for developing better WSSS methods for other medical imaging tasks

Heat transfer performance of lithium bromide solution in falling film generator

An experimental investigation of vertical in-tube falling film heat transfer with different heat fluxes and concentrations of lithium bromide solution were conducted. The experiments show that the heat transfer coefficient increases with the decrease of inlet concentration and significantly increase with heat flux increase. An experimental correlation of falling film heat transfer coefficient is obtained.The comparison of falling film generator with immersed tube generator shows that the heat transfer coefficient is 4.37 times higher than that of immersed tube generator, which can significantly reduce the volume of the falling film generator. The volume of falling film generator is only 52.1% of the volume of immersed tube generator

AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervised semantic segmentation (WSSS) methods with class activation mapping (CAM) have been proposed. However, existing CAM methods suffer from low resolution due to strided convolution and pooling layers, resulting in inaccurate predictions. In this study, we propose a novel CAM method, Attentive Multiple-Exit CAM (AME-CAM), that extracts activation maps from multiple resolutions to hierarchically aggregate and improve prediction accuracy. We evaluate our method on the BraTS 2021 dataset and show that it outperforms state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2306.0547

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications

Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis

Fairness has become increasingly pivotal in medical image recognition. However, without mitigating bias, deploying unfair medical AI systems could harm the interests of underprivileged populations. In this paper, we observe that while features extracted from the deeper layers of neural networks generally offer higher accuracy, fairness conditions deteriorate as we extract features from deeper layers. This phenomenon motivates us to extend the concept of multi-exit frameworks. Unlike existing works mainly focusing on accuracy, our multi-exit framework is fairness-oriented; the internal classifiers are trained to be more accurate and fairer, with high extensibility to apply to most existing fairness-aware frameworks. During inference, any instance with high confidence from an internal classifier is allowed to exit early. Experimental results show that the proposed framework can improve the fairness condition over the state-of-the-art in two dermatological disease datasets.Comment: MICCAI202

Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space

Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logits with Maximum Mean Discrepancy. Moreover, quantitative analysis and experimental results show that our framework has a better property that outperforms previous methods and achieves state-of-the-art on two facial recognition datasets and one animal dataset. Finally, we show experimental results and demonstrate that our debias approach achieves the fairness condition effectively

Long-term follow-up of patients with surgical intractable acromegaly after linear accelerator radiosurgery

Background/PurposeRadiotherapy is a crucial treatment for acromegalic patients with growth hormone (GH)-secreting pituitary tumors. However, its effect takes time. We retrospectively reviewed the long-term outcome of linear accelerator stereotactic radiosurgery (LINAC SRS) for patients with acromegaly from the perspective of biochemical remission and associated factors.MethodsTwenty-two patients presenting with residual or recurrent (GH)-secreting functional pituitary tumor between 1994 and 2004 who received LINAC SRS were enrolled and followed up for at least 3 years. Residual or recurrent tumor was defined as persistent elevated GH or insulin-like growth factor-1 (IGF-1) level and image-confirmed tumor after previous surgical treatment. Biochemical remission was defined as fasting GH less than 2.5 ng/mL with normal sex-and-age adjusted IGF-1.ResultsThe mean follow-up period was 94.7 months (range 36–161 months). Overall mean biochemical remission time was 53 months (median 30 months). Biochemical control was achieved in 15 patients (68.2%) over the follow up period. One patient experienced recurrence after SRS and underwent another operation. Initial GH at diagnosis and pre-SRS GH correlated with biochemical control (p = 0.005 and p < 0.0001, respectively). Further evaluation demonstrated that biochemical control stabilized after 7.5 years. Overall post-SRS hormone deficit persisted in five patients (22.7%).ConclusionIn comparison to other radiosurgery modalities, LINAC radiosurgery also provides a satisfactory outcome. SRS has maximum effect over the first 2 years and stabilizes after 7.5 years. Moreover, SRS elicits long-term biochemical effects and requires longer follow-up for better biochemical remission

Insulin-Mimetic Action of Rhoifolin and Cosmosiin Isolated from Citrus grandis (L.) Osbeck Leaves: Enhanced Adiponectin Secretion and Insulin Receptor Phosphorylation in 3T3-L1 Cells

Citrus grandis (L.) Osbeck (red wendun) leaves have been used in traditional Chinese medicine to treat several illnesses including diabetes. However, there is no scientific evidence supporting these actions and its active compounds. Two flavone glycosides, rhoifolin and cosmosiin were isolated for the first time from red wendun leaves and, identified these leaves are rich source for rhoifolin (1.1%, w/w). In differentiated 3T3-L1 adipocytes, rhoifolin and cosmosiin showed dose-dependent response in concentration range of o.oo1–5 μM and 1–20 μM, respectively, in biological studies beneficial to diabetes. Particularly, rhoifolin and cosmosiin at 0.5 and 20 μM, respectively showed nearly similar response to that 10 nM of insulin, on adiponectin secretion level. Furthermore, 5 μM of rhoifolin and 20 μM of cosmosiin showed equal potential with 10 nM of insulin to increase the phosphorylation of insulin receptor-β, in addition to their positive effect on GLUT4 translocation. These findings indicate that rhoifolin and cosmosiin from red wendun leaves may be beneficial for diabetic complications through their enhanced adiponectin secretion, tyrosine phosphorylation of insulin receptor-β and GLUT4 translocation

Reduction in antioxidant enzyme expression and sustained inflammation enhance tissue damage in the subacute phase of spinal cord contusive injury

<p>Abstract</p> <p>Background</p> <p>Traumatic spinal cord injury (SCI) forms a disadvantageous microenvironment for tissue repair at the lesion site. To consider an appropriate time window for giving a promising therapeutic treatment for subacute and chronic SCI, global changes of proteins in the injured center at the longer survival time points after SCI remains to be elucidated.</p> <p>Methods</p> <p>Through two-dimensional electrophoresis (2DE)-based proteome analysis and western blotting, we examined the differential expression of the soluble proteins isolated from the lesion center (LC) at day 1 (acute) and day 14 (subacute) after a severe contusive injury to the thoracic spinal cord at segment 10. In situ apoptotic analysis was used to examine cell apoptosis in injured spinal cord after adenoviral gene transfer of antioxidant enzymes. In addition, administration of chondroitinase ABC (chABC) was performed to analyze hindlimb locomotor recovery in rats with SCI using Basso, Beattie and Bresnahan (BBB) locomotor rating scale.</p> <p>Results</p> <p>Our results showed a decline in catalase (CAT) and Mn-superoxide dismutase (MnSOD) found at day 14 after SCI. Accordingly, gene transfer of SOD was introduced in the injured spinal cord and found to attenuate cell apoptosis. Galectin-3, β-actin, actin regulatory protein (CAPG), and F-actin-capping protein subunit β (CAPZB) at day 14 were increased when compared to that detected at day 1 after SCI or in sham-operated control. Indeed, the accumulation of β-actin⁺immune cells was observed in the LC at day 14 post SCI, while most of reactive astrocytes were surrounding the lesion center. In addition, chondroitin sulfate proteoglycans (CSPG)-related proteins with 40-kDa was detected in the LC at day 3-14 post SCI. Delayed treatment with chondroitinase ABC (chABC) at day 3 post SCI improved the hindlimb locomotion in SCI rats.</p> <p>Conclusions</p> <p>Our findings demonstrate that the differential expression in proteins related to signal transduction, oxidoreduction and stress contribute to extensive inflammation, causing time-dependent spread of tissue damage after severe SCI. The interventions by supplement of anti-oxidant enzymes right after SCI or delayed administration with chABC can facilitate spinal neural cell survival and tissue repair.</p