MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis

There is no doubt that advanced artificial intelligence models and high quality data are the keys to success in developing computational pathology tools. Although the overall volume of pathology data keeps increasing, a lack of quality data is a common issue when it comes to a specific task due to several reasons including privacy and ethical issues with patient data. In this work, we propose to exploit knowledge distillation, i.e., utilize the existing model to learn a new, target model, to overcome such issues in computational pathology. Specifically, we employ a student-teacher framework to learn a target model from a pre-trained, teacher model without direct access to source data and distill relevant knowledge via momentum contrastive learning with multi-head attention mechanism, which provides consistent and context-aware feature representations. This enables the target model to assimilate informative representations of the teacher model while seamlessly adapting to the unique nuances of the target data. The proposed method is rigorously evaluated across different scenarios where the teacher model was trained on the same, relevant, and irrelevant classification tasks with the target model. Experimental results demonstrate the accuracy and robustness of our approach in transferring knowledge to different domains and tasks, outperforming other related methods. Moreover, the results provide a guideline on the learning strategy for different types of tasks and scenarios in computational pathology. Code is available at: \url{https://github.com/trinhvg/MoMA}.Comment: Preprin

Localized Dielectric Loss Heating in Dielectrophoresis Devices

Temperature increases during dielectrophoresis (DEP) can affect the response of biological entities, and ignoring the effect can result in misleading analysis. The heating mechanism of a DEP device is typically considered to be the result of Joule heating and is overlooked without an appropriate analysis. Our experiment and analysis indicate that the heating mechanism is due to the dielectric loss (Debye relaxation). A temperature increase between interdigitated electrodes (IDEs) has been measured with an integrated micro temperature sensor between IDEs to be as high as 70 °C at 1.5 MHz with a 30 Vpp applied voltage to our ultra-low thermal mass DEP device. Analytical and numerical analysis of the power dissipation due to the dielectric loss are in good agreement with the experiment data

Hover-Net : simultaneous segmentation and classification of nuclei in multi-tissue histology images

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then, for each segmented instance the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels

The COOH-terminus of TM4SF5 in hepatoma cell lines regulates c-Src to form invasive protrusions via EGFR Tyr845 phosphorylation

AbstractTransmembrane 4 L six family member 5 (TM4SF5) enhances cell migration and invasion, although how TM4SF5 mechanistically mediates these effects remains unknown. In the study, during efforts to understand TM4SF5-mediated signal transduction, TM4SF5 was shown to bind c-Src and thus hepatoma cell lines expressing TM4SF5 were analyzed for the significance of the interaction in cell invasion. The C-terminus of TM4SF5 bound both inactive c-Src that might be sequestered to certain cellular areas and active c-Src that might form invasive protrusions. Wildtype (WT) TM4SF5 expression enhanced migration and invasive protrusion formation in a c-Src-dependent manner, compared with TM4SF5-null control hepatoma cell lines. However, tailless TM4SF5ΔC cells were more efficient than WT TM4SF5 cells, suggesting a negative regulatory role by the C-terminus. TM4SF5 WT- or TM4SF5ΔC-mediated formation of invasive protrusions was dependent or independent on serum or epidermal growth factor treatment, respectively, although they both were dependent on c-Src. The c-Src activity of TM4SF5 WT- or TM4SF5ΔC-expressing cells correlated with enhanced Tyr845 phosphorylation of epidermal growth factor receptor. Y845F EGFR mutation abolished the TM4SF5-mediated invasive protrusions, but not c-Src phosphorylation. Our findings demonstrate that TM4SF5 modulates c-Src activity during TM4SF5-mediated invasion through a TM4SF5/c-Src/EGFR signaling pathway, differentially along the leading protrusive edges of an invasive cancer cell

EFFECTIVE DOSE MEASUREMENT FOR CONE BEAM COMPUTED TOMOGRAPHY USING GLASS DOSIMETER

During image-guided radiation therapy, the patient is exposed to unwanted radiation from imaging devices built into the medical LINAC. In the present study, the effective dose delivered to a patient from a cone beam computed tomography (CBCT) machine was measured. Absorbed doses in specific organs listed in ICRP Publication 103 were measured with glass dosimeters calibrated with kilovolt (kV) X-rays using a whole body physical phantom for typical radiotherapy sites, including the head and neck, chest, and pelvis. The effective dose per scan for the head and neck, chest, and pelvis were 3.37±0.29, 7.36±0.33, and 4.09±0.29 mSv, respectively. The results highlight the importance of the compensation of treatment dose by managing imaging dose

A Case of Laparoscopic Radical Prostatectomy for a Prostatic Stromal Tumor of Uncertain Malignant Potential

Prostatic stromal tumor of uncertain malignant potential (STUMP) is a rare neoplasm with distinctive clinical and pathological characteristics. Here we report a case of laparoscopic radical prostatectomy performed in a patient with prostatic STUMP

Domain Generalization in Computational Pathology: Survey and Guidelines

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause \emph{domain shift} (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative \emph{domain generalization} (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.Comment: Extended Versio

Breathing silicon anodes for durable high-power operations

Silicon anode materials have been developed to achieve high capacity lithium ion batteries for operating smart phones and driving electric vehicles for longer time. Serious volume expansion induced by lithiation, which is the main drawback of silicon, has been challenged by multi-faceted approaches. Mechanically rigid and stiff polymers (e.g. alginate and carboxymethyl cellulose) were considered as the good choices of binders for silicon because they grab silicon particles in a tight and rigid way so that pulverization and then break-away of the active mass from electric pathways are suppressed. Contrary to the public wisdom, in this work, we demonstrate that electrochemical performances are secured better by letting silicon electrodes breathe in and out lithium ions with volume change rather than by fixing their dimensions. The breathing electrodes were achieved by using a polysaccharide (pullulan), the conformation of which is modulated from chair to boat during elongation. The conformational transition of pullulan was originated from its a glycosidic linkages while the conventional rigid polysaccharide binders have beta linkagesopen1