dataesr/scientific-paragraphs-categorization huggingface dataset:
However, our results may include some biases. Indeed our work pointed out the limitations to use ImQuest with nuclear medicine images and showed why the IEC NEMA phantom was perfectible; but also offered improvement tracks: -ImQuest is dedicated to CT imaging, thus we needed to convert DICOM header to upload nuclear imaging data. The software only provides UH as voxel intensity. -TTF estimation it sensitive to noise conditions. As mentioned in the AAPM task group 233 report, a CNR of at least 15 is needed between the task and the background for a relevant estimation of TTF  [38]. In our case, a single slice was sufficient to reach a CNR of about 40-45 for F3D and xS and about 75 for xB [Fig. 6d ]. However, performing TTF over a spherical object is not ideal because of axial partial volume effect and sphere deformation [Fig.
The subjective comparison of these two modalities is however limited by two factors: first, nuclear physicians are used to the contrast and texture of F3D images, whereas xB images appear to be new and would require a learning period; second, no or partial gold standard is usually available    in oncological bone scan studies. Concerning daily 99m Tc-bone imaging in our department, NAC and AC F3D reconstructions are still considered as the gold standard; xS is not used at all; and if xB arouses interests especially because of the high spatial resolution, the different soft-tissue/bone and bone/bone contrast, and for complex cases, it remains dedicated to clinical research. Broadquant module is activated once a week in anticipation of quantitative studies, and for every patient included in a Xofigo® trial.
Lutathera post-treatment 177 Lu-NET imaging is systematically reconstructed with the Broadquant module, physicians using quantitative xS images for the interpretation. Tumor SUV are not mentioned in the medical report but are sometimes measured to validate the delta uptake through the cures. Comparing F3D and xS imaging revealed Gibbs artifacts involving an underestimation of the physiological expected uptake in the tissue around the lesions [Fig. 9. a]. In a context of clinical research, healthy organs uptake measurements helped to predict subacute hematological toxicity associated with this therapy  [45]. The question of the implementation of SPECT quantification opens promising perspectives  [46]. In bone scintigraphy, quantitative bone SPECT/CT could provide helpful indices for the prognostication of bone metastatic burden  [47] , and its follow-up through therapy.
Numerical experiments In this section, we numerically study the discrete model's convergence towards the continuous one. Then, we investigate the influence of the two parameters ω and ε on the system and on its displacement, while the rest of the swimmer is determined by the values in table  1. All simulations are achieved using Matlab. We consider here that the default length L of the active arm is small compared to the rest of the swimmer. The first sphere thus acts like the head of a sperm cell, and the active arm like a link between the head and the flagella, which gives a signal so that the rest of the system oscillates.
We analyzed the changes in p-glycoprotein (P-gp / CD243) and the low-density lipoprotein receptor-related protein (LRP, CD91) following cell stimulation with nsPEF and its implication in paclitaxel chemotherapy in vitro. A confocal microscope was used to examine pan-cadherin and N-cadherin immunoreactivity after nsPEF treatment. These results were compared to data on formation and growth of pancreatic cancer spheroids in order to assess cell adhesive properties.
Holotomographic microscopy Live holotomography was performed using a 3D Cell Explorer microscope (Nanolive SA, Ecublens, Switzerland). Pancreatic cancer cells were imaged using 35 mm Ibidi glass-bottom μ-Dish dishes (Ibidi GmbH, Germany). During imaging, temperature was set to 37 °C and controlled using an Ibidi Heating System (Ibidi GmbH, Germany), while the su cient amount of CO 2 was maintained by using a CO 2independent culture medium (Sigma-Aldrich). Initially, we placed the electroporation needles surrounding the visualized cell groups. Afterwards, we started to recorded a video and after a short while treated cells with PEFs. The snapshots were taken each 1 minute for at least 13 minutes. Further, the data from the microscope was gathered and analyzed using STEVE Software. Concerning the Yo-Pro-1 dye uptake studies, the cells were permeabilized according to the description of permeabilization of cell suspension.
Molecular dynamics simulation of membrane response The molecular dynamics simulations were performed with GROMACS 2018. 3 software  [54]  on the calculational cluster in the Department of Theoretical Chemistry and Physics at the Lorraine University. The models for simulations were built with CHARMM-GUI web software and visually inspected with VMD software  [55] ,  [56]. The simulated systems were composed of a membrane in the ionic water solution. The membrane was composed of ~256 lipids per membrane layer. All the systems were built considering mixture of lipids-cholesterol and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine -POPC. Two systems representing 10% and 30% cholesterol were modeled, each with POPC. Before the simulation, the system was solvated in physiological conditions of NaCl water (TIP3) solution.
CellcyteX studies CellcyteX live-cell imaging system acquires phase contrast images, which are analyzed with detection software. Detection software was used to determine cell viability by measuring cell con uency and comparing the results with cell count and nuclei studies. The system was used to assess the kinetics of cells' growth after treatment with nsPEF and Paclitaxel. In the study we aimed to establish the optimal time for the viability tests and examine if the antiproliferative effect of nsPEF is permanent. Control and PEF-treated cells were seeded on 96-well plates. After 24 hours at 37°C paclitaxel was added to wells and cells were further incubated at 37 °C in the incubator set to work with CellcyteX operating Cellcyte Studio software. The experiment was performed in replicates and each well consisted of four, square zones, in which the cells were counted.
Acknowledgements The authors would like to acknowledge  Paweł Kazulo  for help in the CellcyteX data analysis. This research project is nanced by the  National Science Centre (Poland)  No. 2017/27/N/NZ3/01110  (to Olga Michel). This research project is nanced by the  National Science Centre (Poland)  No. 2017/27/N/NZ3/01110  (to Olga Michel). The authors have no relevant nancial or non-nancial interests to disclose. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by  Wojciech Szlasa ,  Natalia Sauer  and  Olga Michel. The rst draft of the manuscript was.
, 2022)  and presented at MICCAI 2022 in Singapore. The international challenge provided a scientific platform for the development of deep learning solutions for surgical action triplet detection in the OR. During the contest, registered participants were granted privileged access to a part of the CholecT50 dataset  (Nwoye et al. , 2022b)  used as training data. The provided data labels are based on binary presence only, motivating the contestants to innovate by proposing alternative approaches, such as weak supervision, for modeling the localization aspect of the task. Meanwhile, some spatially annotated mini-samples of the dataset are provided for checks and validation. Useful code repositories, a validation system, a discussion forum, a submission server, support, and other helpful resources were also provided.
08-4. 49% on triplet detection using average precision metrics at a threshold of 0. 5 IoU, which is very strict for weakly supervised methods. We also illustrate, using a rich selection of qualitative results, the behavior of proposed methods under various visual and procedural challenges associated with surgical video datasets. We highlight some strategic findings on the suitability of the observed computer vision techniques for surgical workflow activity modeling and discuss the significance and relevance of the presented results to the advancement of AI in surgery. We conclude with a survey polling the participants on their experiences and how to standardize and improve future events. The rest of the paper is organized as follows: a review of related works in the next section helps to position our work in the research domain.
3D CNN  (Tran et al. , 2015; Carreira and Zisserman, 2017; Xie et al. , 2018; Feichtenhofer et al. , 2019 ) take a different approach by using spatio-temporal convolutions. Recent developments led to the video Transformers  (Bertasius et al. , 2021a; Liu et al. , 2022b) , built around spatio-temporal attention mechanisms. In surgical computer vision, the most common activity recognition task is phase recognition. Several models were proposed for this task:  Twinanda et al. (2016)  used the same concept as the LRCN  (Donahue et al. , 2015) , combining a ResNet-152 model with an LSTM on the Cholec80 dataset. Variations of the same architecture were employed in subsequent works on the same dataset  (Funke et al. , 2018; Yu et al. , 2019; Jin et al. , 2018b Jin et al. , , 2021;; Gao et al. , 2021).
Datasets: from recognition to detection Action datasets offer a large choice of tasks to be learned and performed by algorithms, with variations in granularity as well as the nature of the proposed task. From general computer vision, one early example of action classification datasets is PAS-CAL VOC  (Everingham et al. , 2005) , with 10 action classes and static images only. To better capture actions, video datasets were released: UCF-101  (Soomro et al. , 2012) , followed by Charades  (Sigurdsson et al. , 2016)  and Kinetics  (Carreira and Zisserman, 2017). Another solution for capturing more information on actions is to add spatial detection: V-COCO  (Gupta and Malik, 2015)  extended the original COCO dataset  (Lin et al. , 2014)  with bounding boxes around interacting elements. HICO-DET  (Chao et al.
, 2015) , centered around human-object interaction, offered similar annotations. Video datasets with spatial action detection, such as UCFSports  (Soomro and Zamir, 2014)  or AVA  (Gu et al. , 2018)  exist as well. Action recognition in the surgical domain mainly comes in the form of surgical phase classification, as proposed by the Cholec80 dataset  (Twinanda et al. , 2016)  dividing cholecystectomy into 7 phases. The CATARACTS dataset  (Al Hajj et al. , 2019)  contained similar phase annotations for cataract surgery. Finer-grained descriptions of activities were featured in the By-pass40 dataset  (Ramesh et al. , 2021)  via surgical steps. Additional information can also come in the form of spatial detections:  Vardazaryan et al. (2018) ;  Nwoye et al. (2019)  used an extension of Cholec80 with instrument bounding boxes on the test set, for the evaluation of weakly supervised object detectors; however, those only located actions in an indirect manner.
Actual action localization was offered by the SARAS-ESAD dataset  (Bawa et al. , 2021; Lin et al. , 2022) , with bounding boxes pointing to action verbs being performed. Overall, action datasets have evolved towards more detailed and complex tasks, gradually adding more information in the form of interaction labels or bounding boxes. Our work continues in this direction by proposing a weakly supervised action localization task on the CholecT50 dataset  (Nwoye et al. , 2022b).
Benchmark challenge: from recognition to detection International challenges have become a de facto standard for benchmarking image analysis algorithms  (Wiesenfarth et al. , 2021). They have a significant impact on the research community, oftentimes, encouraging a surge in new research directions owing to their characteristic creation of shared datasets, as well as incentives for best-performing algorithms. And more significantly, they offer a more reliable result benchmarking by withholding their test data from the public domain and comparing rival algorithms using the same criteria. The PASCAL VOC challenge  (Everingham et al. , 2005)  and the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)  (Russakovsky et al. , 2015)  are among the earliest image analysis challenges featuring deep learning methods albeit focusing on simple classification.
Subsequent challenges advance the recognition task to object detection  (Lin et al. , 2014; Everingham et al. , 2010) , single and multi-object tracking  (Dendorfer et al. , 2020; Chen et al. , 2021; Kristan et al. , 2016) , segmentation  (Cordts et al. , 2016; Voigtlaender et al. , 2019) , etc. , spearheading the development of state-of-the-art models. Repeating some challenges over the years comes with either or both increasing the size of datasets and task difficulty. The first MICCAI grand challenge organized in 2007 in Brisbane brought similar benefits to biomedical image analysis. Notable in the surgical data science domain is the M2CAI 2016 challenge 2 featuring both surgical workflow analysis and tool presence detection for laparoscopic cholecystectomy leading to the creation of widely used datasets in the field: m2cai16-workflow  (Stauder et al.
, 2016; Twinanda et al. , 2016), m2cai16-tool and Cholec80 (Twinanda et al. , 2016). Other datasets such as BraTS  (Menze et al. , 2014) , CATARACTS  (Al Hajj et al. , 2019) , ROBUST-MIS  (Roß et al. , 2021) , SurgVisDom  (Zia et al. , 2021) , MISAW (Huaulmé et al. ,   2 http://camma. u-strasbg. fr/m2cai2016/ 2021), HeiChole  (Wagner et al. , 2021) , SARAS-ESAD  (Bawa et al. , 2021) , Robotic Instrument Segmentation  (Allan et al. , 2019) , CaDIS  (Grammatikopoulou et al. , 2019; Luengo et al. , 2021) , etc, are all products of biomedical challenges. In 2021, we introduce the first endoscopic vision challenge focusing on the recognition of surgical activities in the form of triplets  (Nwoye et al. , 2022a). A rerun of this challenge in the current edition is with two notable advances: (1) the increment of the task difficulty from presence to spatial detection, which also increases the task usefulness, and (2) the addition of spatial bounding box labels to challenge evaluation data thereby enriching the dataset.
Dataset and spatial annotation The challenge experiments are conducted on CholecT50  (Nwoye et al. , 2022b) , the largest endoscopic video dataset for surgical action triplet recognition. The dataset consists of 50 video footages of laparoscopic cholecystectomy that has been annotated with 100 distinct categories of surgical action triplets. At 1 frame per second (fps), a total of 100. 9K frames of the dataset has been annotated with ∼151K triplet instances formed from 6 instrument, 10 verb, and 15 target categories. We follow the official data splits  (Nwoye and Padoy, 2022)  for challenge purposes (aka CholecT50-challenge version) which ensures that the test set is drawn from only the 5 videos that are not in the public domain for fairness in the competition. The rest of the 45 videos, aka CholecT45, are released to the participants for training their models.
The CholecT45 provides only binary presence labels for the triplets, its individual components (instruments, verbs, targets), and the phase labels. Without spatial labels in the training data, the challenge allows for the modeling of the instrument's localization by weak supervision, thus, alleviating the cost and tedious annotation effort. While pretraining/transfer learning on third-party spatially annotated datasets is allowed, the downstream task involves learning the instrument localization and triplet-box association from imperfect annotations. For evaluation, we annotate the test set with bounding boxes over the instruments using the IHU MoSaiC video annotation tool for surgical datasets. Conventionally, an instrument can be divided into two sub-parts: effector, which is the instrument tip responsible for primary action on anatomy, and shaft which connects the instrument tip with the instrument handle on its other end.
As an annotation protocol, we consider only the effector part of the instrument and draw bounding boxes for instrument tips: (cold) grasper, bipolar (grasper), (monopolar) hook, (monopolar) scissors, clipper (short for clip applier), and irrigator (short for suction/irrigation device). Further, we match the bounding box with the corresponding triplet label and the annotations are stored in JSON format that includes both triplet binary presence labels as well as bounding box details. To give participants an insight into the testing labels, a mock-up validation set is generated. This involves 5 short video clips with triplet binary presence labels, instrument's bounding box labels, and box-triplet matching labels. The validation set's spatial annotation is outsourced from the overlapping videos of the m2cai16-tool-location dataset  (Jin et al.
Validation system In the validation step, participants were tasked with testing their docker containers using a validation script, to ensure that they properly ingested input data as well as adhered to the proper specifications for their model outputs. This validation script, implemented in Python, ensured that the participants' models respected the specified compute constraints, loaded and output data properly, and ran within the specified evaluation time and resource limits. The pipeline of the validation system is illustrated in Figure  3. To aid participants, we split the validation phase into two parts: a local phase and a host phase. During the local validation phase, we gave the participants access to a properly formatted validation dataset which they could use as a reference to develop their models and docker container.
Participation statistics During the call for participants, a total of 32 teams registered to participate in the challenge. Of these teams, a total of 11 teams progressed beyond the validation phase; as detailed in Fig 2, these teams were drawn from 9 different countries and 3 continents. This final participation was just over half of the CholecTriplet 2021 challenge  (Nwoye et al. , 2022a) ; we also observed a drop in the relative ratio between the final participants and total registrants (45. 5% in 2021; 34. 4% in 2022). We identified 2 likely reasons for the drop in each of these statistics: (1) the added difficulty of surgical triplet localization, which could have deterred registered teams from progressing to the validation phase, and (2) the use of almost the same dataset as in the 2021 edition, which may have reduced the perceived novelty of the challenge.
Methodology A summary of each participating team's proposed method is provided in Table  2 ; in the following subsections, we elaborate on these summaries, providing detailed descriptions of each method and a comparative analysis. This is proposed by the challenge organizers as a proof-ofconcept and as a baseline to the competing methods. The RDVdet, shown in Fig. 4 , is a detection version of the Rendezvous  (Nwoye et al. , 2022b)  model popular for surgical action triplet recognition. Notably, the Transformer-inspired RDV is conceptually made up of 3 major components: (1) a weakly supervised  To advance this model for detection, the instruments are first localized via the WSL layer. Here, the interest is in the last 6-channel convolution layer designed to learn the location of each of the six distinct instrument categories in the CholecT50 dataset in the form of class activation maps (CAM).
Initially, a YOLOv5 teacher model is pretrained on three external robotic instrument segmentation datasets  (Allan et al. , 2020 (Allan et al. , , 2019;; Bodenstedt et al. , 2015)  for which bounding box annotations are automatically generated from the original segmentation masks. This teacher model is then used to iteratively generate pseudo-labels on the challenge training set, which are in turn used to train a student model (also a YOLOv5 detector). For the pseudo-label generation, the teacher predictions are filtered based on the prediction confidence and following the ground truth instrument annotations.
ResNet-CAM-YOLOv5 ResNet-CAM-YOLOv5 combines class activation mapping with supervised pre-training on external datasets to obtain accurate instrument localization, then employs a multi-task approach for triplet recognition. Specifically, for triplet recognition, a multi-task ResNet50 model is trained on phase recognition, instrument classification, verb classification, and target classification, as in several other methods. In addition, rather than including a module to associate the predicted instrument, verb, and target into a predicted triplet, it includes another branch for direct triplet classification. For triplet localization, a YOLOv5 instrument detector is first pre-trained on instrument bounding boxes from the Cholec-Seg8k  (Hong et al. , 2020)  and HeiCo  (Maier-Hein et al. , 2021)  datasets; then, to obtain a model that localizes instrument tips rather than the entire instrument, the YOLOv5 model is finetuned using the LapChole  (Stauder et al.
, 2016)  and Endovis Instrument  (Bodenstedt et al. , 2015)  datasets. During inference, to predict the instrument class for each detection, the YOLO-detected bounding boxes are matched with instrument class activation maps ResNet using a threshold-based decision tree, which considers the scores of the recognized instrument classes, the scores of detected bounding boxes, and the mean of the class activation in the region of the detected bounding box. Meanwhile, for triplet classification, the highest-scoring triplet (obtained from the triplet classification branch) that includes the previously predicted instrument class is predicted as the triplet.
Transfer learning Transfer Learning is a de facto approach to utilize models trained on one task such as object detection for another related task such as instrument/triplet detection. Distilled-Swin-YOLO and ResNet-CAM-YOLOv5 train YOLOv5, a state-of-the-art object detector, on external laparoscopic datasets as well as on the challenge training data for triplet localization. This allows the model to exploit YOLOv5's capabilities to better detect instruments. A rudimentary form of transfer learning, known as pre-training, involves prior training of a model on another dataset, usually a larger one, to mitigate overfitting. Rather than actual model training, pre-training is most approached indirectly via full or partial weights initialization; especially for the backbone parameters. 72. 7% models presented in this challenge are pretrained on ImageNet; Cholec80 and other endoscopic datasets make up the rest.
Levels of model supervision Alternatives to full supervision are highly sought after in surgical data science due to the heavy cost of manually labeled surgical data. In that spirit, no labels at all were provided for the localization task of this challenge, which left no possibility for true full supervision of challenge entrants. To solve this, weak supervision based on Class Activation Maps (CAM) was an overwhelmingly popular choice. The activation in CAMs manifests as a blob covering the entity to detect; further manipulations of the CAM, e. g. fitting a bounding box to the blob, enable spatial detection at no annotation cost since CAMs are a byproduct of training for binary presence detection. We observed that 7 methods used this type of CAM-based weak supervision: SurgNet, IF-Net, MTTT, EndoSurgTRD, DATUM, DualMFFNet, and RDV-Det.