Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

AbstractMitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by ∼5% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.Abstract Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by ∼5% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64

Korean Medicine Subcision Therapies in Scar Treatment: A Retrospective, Multicenter Study at Network Clinics

Jungsang Kim,1,2,* Ju-Hyun Lee,3,* Dongbin Jeong,4 Taekyung Lim,4 Sangwoo Jung,4 Kwongil Paeng,4 Sangyoup Lee,4 Hyunki Cho,4 Seungyoup Lee,4 Ikdu Kim,4 Byungsoo Kang,5,6 Jae Hyo Kim,7 Hongmin Chu,8 Museok Hong4 1Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea; 2Daeat Korean Medicine Clinic, Suwon, Kyunggi-do, Republic of Korea; 3Department of Medical Support, Imsil-Gun Medical Center, Imsil, Jeollabuk-do, Republic of Korea; 4Rodam Korean Medical Clinic Network, Seoul, Republic of Korea; 5Daeat Korean Medicine Hospital, Seoul, Republic of Korea; 6Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea; 7Department of Meridian & Acupoint, College of Korean Medicine, Wonkwang University, Iksan-si, Jeollabuk-do, Republic of Korea; 8Korean Medicine Convergence Research Information Center for Stroke, College of Korean Medicine, Wonkwang University, Gwangju, Republic of Korea*These authors contributed equally to this workCorrespondence: Hongmin Chu, Korean Medicine Convergence Research Information Center for Stroke, College of Korean Medicine, Wonkwang University, Hoejaero 1140-23, Republic of Korea, Email [email protected] Museok Hong, Department of Korean Internal Medicine, College of Korean Medicine, Wonkwang University, 460, Iksan-daero, Iksan-si, Jeollabuk-do, Republic of Korea, Email [email protected]: This study is a retrospective, multicenter research designed to report the efficacy of Korean medicine subcision therapies in scar treatment.Patients and Methods: Charts and photographs of 29 patients who received subcision treatment between May 2016 and June 2020 in four scar treatment network clinics were analyzed. The Qualitative Global Acne Scarring Grade System (QGASC) and the Stony Brook Scar Evaluation Scale (SBSES) were used to objectively measure scar scores.Results: Except for 4 patients whose GASGS and SBSES scores remained unchanged, most patients’ scars showed improvement from Visit 2 to about Visit 8. Furthermore, the degree of change for both scales was found to be statistically significant.Conclusion: Subcision therapy using acupuncture has been found to be an effective treatment for scar, with statistically significant improvements in patients’ SBSES and QGASC scores.Keywords: scar, Korean medicine, subcision, acupunctur

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) >/= 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS >/= 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN

Entropic force approach to noncommutative Schwarzschild black holes signals a failure of current physical ideas

Recently, a new perspective of gravitational-thermodynamic duality as an entropic force arising from alterations in the information connected to the positions of material bodies is found. In this paper, we generalize some aspects of this model in the presence of noncommutative Schwarzschild black hole by applying the method of coordinate coherent states describing smeared structures. We implement two different distributions: (a) Gaussian and (b) Lorentzian. Both mass distributions prepare the similar quantitative aspects for the entropic force. Our study shows, the entropic force on the smallest fundamental unit of a holographic screen with radius $r_0$ vanishes. As a result, black hole remnants are unconditionally inert even gravitational interactions do not exist therein. So, a distinction between gravitational and inertial mass in the size of black hole remnant is observed, i.e. the failure of the principle of equivalence. In addition, if one considers the screen radius to be less than the radius of the smallest holographic surface at the Planckian regime, then one encounters some unusual dynamical features leading to gravitational repulsive force and negative energy. On the other hand, the significant distinction between the two distributions is conceived to occur around $r_0$, and that is worth of mentioning: at this regime either our analysis is not the proper one, or non-extensive statistics should be employed.Comment: 15 pages, 2 figures, new references added, minor revision, Title changed, to appear in EPJ Plu