San diego pathology conference

China ; Jun Shi, Hefei Univ. BYOL is first used to initialize the image representations. Then, a contrastive dynamic clustering CDC module is proposed to enable the weak-supervision task to be trained as the full-supervision paradigm. Experiments in a endometrial dataset consisting of WSIs show that the proposed method is competitive to the full-supervision method, and achieves a Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach.

Nacional de Colombia Colombia. The basic idea is to integrate the training process with a soft-clustering approach and an objective selection of the obtained clusters that better represent the whole variability. This strategy considerably reduces the training set while the classification performance is improved when classifying patches with ovarian cancer. Automatic flagging of AI segmentation errors in computational pathology.

Tau protein discrete aggregates in Alzheimer's disease: neuritic plaques and tangles detection and segmentation using computational histopathology. Author s : Kristyna Manouskova, Sorbonne Univ. Using histological whole slide images annotated by specialists, we trained deep learning models and created a state-of-the-art pipeline to detect and segment these objects. The spatial distribution and form of the proteins is hypothesised to be correlated with the advancement of the disease. Posters will be on display Sunday and Monday with extended viewing until pm on Sunday.

The poster session with authors in attendance will be Monday evening from to pm. Award winners will be identified with ribbons during the reception. Award announcement times are listed in the conference schedule. Session 3: Automated Quantification of Tissue Biomarkers. Author s : Thomas E. Burks, Wake Forest Univ. Wexner Medical Ctr.

United States ; Metin N. Gurcan, M. School of Medicine United States. Pan-cytokeratin IHC staining increases agreement but is costly, nonroutine, and yields false positives, complicating algorithm development. Author s : David R. Brimhall, Edward A. Medina, The Univ. Our method was the top-performing algorithm of all submissions to the BreastPathQ challenge, achieving a prediction probability of 0.

WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need. Deep ordinal regression for automatic tumor cellularity assessment from pathological images. The assessment of cellularity is an important component of tumor burden assessment. We present a deep ordinal regression framework to automatically assess cellularity from pathological images.

We formulate the cellularity assessment as an ordinal regression problem and address by an end-to-end learning approach using deep convolutional neural networks. We evaluated the proposed methods on the SPIE BreastPathQ dataset and achieved significant higher agreement with expert pathologist scoring. Session 4: Multispectral, Multimodality, and Fused Imaging.

In the use of Artificial Intelligence and hyperspectral imaging in digital pathology for breast cancer cell identification. Specifically, HSI technology applied to breast cancer histology, may significantly reduce the time of tumor diagnosis, minimizing the histopathology current bottleneck in cancer diagnosis.

In this work, breast tumor slides have been digitally scanned to whole-slides and further annotated at cell level. The annotated regions have also been captured with an HS microscopic acquisition system. Supervised spectral and spatial-spectral classifications were carried out to automatically detect tumor cells from the rest of the coexisting cells in breast histological samples.

Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images. Author s : Ruining Deng, Vanderbilt Univ. Medical Ctr. United States ; Lee E. Wheless, Agnes B.

Fogo, Vanderbilt Univ. Currently, 3D glomerular identification and reconstruction of large-scale glomeruli are labor-intensive tasks, and time-consuming by manual analysis on whole slide imaging in 2D serial sectioning representation. Moreover, there are no approaches to present 3D glomerular visualization for human examination.

In this paper, we introduce an end-to-end holistic deep-learning-based method that achieves automatic detection, segmentation and multi-object tracking of individual glomeruli with large-scale glomerular-registered assessment in a 3D context on WSIs. The high-resolution WSIs are the inputs, while the outputs are the 3D glomerular reconstruction and volume estimation. This pipeline achieves Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning.

Whole slide tumor margin classification with hyperspectral imaging and deep learning. Author s : Minh Tran, The Univ. From 33 fixed tissues from patients with thyroid cancer, we produced three different datasets: an RGB image dataset that was acquired from a whole slide image scanner, a hyperspectral HS dataset that was acquired with a compact hyperspectral camera, and a HS-synthesized RGB image dataset. This study demonstrates hyperspectral images can improve cancer classification performance.

Tumor resection margin assessment through X-ray phase-contrast micro-CT. Hospital Huddinge Sweden ; Jakob C. Hospital Huddinge Sweden ; Hans M. For fast assessment of the resection margins, we propose virtual histology using x-ray phase-contrast micro-CT. We demonstrate phase-contrast CT of formalin-fixed paraffin-embedded tumors from liver and pancreas and compare with classical histology images.

The agreement between CT and microscopy images is excellent and we conclude that phase-contrast micro-CT offers a complement to microscopy for histopathological assessment of tumors.

Cell phenotyping using unsupervised clustering on multiplexed fluorescence images of breast cancer tissue specimens. Yaffe, Anne L. Literature illustrated the usability for unsupervised algorithms for cell phenotyping by validating the results against manual gated cell populations. To extend the knowledge for identifying unknown inclusive cell populations in our database of multiplexed immunofluorescence images of breast cancer tissue microarrays, we explored two commonly used methods PhenoGraph and FlowSOM using reference standard of clinical relevant cancer subtypes that were manually assigned based on immunohistochemistry scoring of serial sections.

Our results showed PhenoGraph yielded better results but much larger variations using different parameter settings than FlowSOM. Inpainting missing tissue in multiplexed immunofluorescence imaging. Coburn, Keith T. Wilson, Joseph T. Roland, Vanderbilt Univ. United States ; Bennett A. Landman, Yuankai Huo, Vanderbilt Univ. However, with multiple rounds of staining and bleaching, it is inevitable that the tissue may be physically depleted.

A digital way of synthesizing such missing tissue is appealing. We investigate the feasibility of GAN approaches to synthesize missing tissues using 11 MxIF structural molecular markers. We integrate a multi-channel high-resolution image synthesis approach to synthesize the missing tissue marker from the remaining markers.

The performance of different methods is quantitatively evaluated via the downstream cell membrane segmentation task. Spatial analysis of cellular arrangement using quantitative, single-cell imaging of protein multiplexing. We have developed methods that can not only assess the densities of various immune cell types in the tumor microenvironment, but also quantify their cellular arrangement and spatial relationships.

We investigated and compared using binary cell counts and clustering methods to identify cells with various marker-co-expression signatures.

Their localizations in the lesion were assessed with neighborhood analysis and cell-to-cell distance mapping. The correlation of immune phenotype and spatial patterns to clinical outcomes will be evaluated. Personalized stain style transfer layers for distributed and heterogeneous histology slides. Pathology Visons will continue as an in-person meeting this October. We are closely monitoring the COVID pandemic and will take the necessary precautions to ensure the health and safety of our attendees.

Should there be any change to PV21, we will promptly notify those impacted. Head and Neck Pathology. Hepatic Pathology. Infectious Disease. Laboratory Medicine. Laboratory Science.

Liver Pathology. Lung Pathology. Medical Knowledge. Medical Technologists. Medical Technology. Molecular Biology. Molecular Pathology.

Multi-Specialty Pathology. Neoplastic Hematopathology. Neoplastic Pulmonary Pathology. Nurse Practitioners. Pancreaticobiliary Pathology.

Pathology of Pancreas. Patient Care. Pediatric Pathologists. Physician Assistant. Physicians in Training. Placental Pathology. Primary Care. January 6 Razelle Kurzrock, M. January 13 Stephanie Cherqui, Ph. January 27 Ron Firestein, M. February 3 Linda Ferrell, M. February 10 Rafael Bejar, M. February 24 Christopher C. Glembotski, Ph. March 3 Shane Meehan, M. March 10 Catriona Jamieson, M. March 17 Alissa Weaver, M.

March 24 Steven Finkbeiner, M. March 31 Sarah Murray, Ph. April 7 Terry Gaasterland, Ph. April 14 Judith Varner, Ph. April 21 Marta Margeta, M. April 29 - Special Lecture at p. Kevin Roth, M. May 5 Pascal Gagneux, Ph. May 19 Elizabeth A. Winzeler, M. June 2 Kelly Frazer, Ph. June 9 Brett Lowenthal, M. June 16 Uptal Das, Ph. June 23 Ken Fujimura, Ph. June 30 Karra Muller, M.

September 10, Lawrence Goldstein, Ph. September 17, David Pride, M. Wednesday, September 19, noon to p. September 24, Hua Yu, Ph. Wednesday, September 26, noon to p. October 1, Cristian Achim, M. October 8, Maike Sander, M. October 15, Rachel Schrier, Ph. October 22, Andrew Kummel, Ph. October 29, Sujan Shersta, Ph. November 19, Nunzio Bottini, M. December 3, Jonathan Lin, M.

January 7, R. Michael Roberts, Ph. January 14, Alysson Muotri, Ph. January 28, Mana Parast, M. February 4, Albert La Spada, M. February 11, Michael Busch, M.

February 25, Kumar Sharma, M. March 4, Yang Xu, Ph. March 11, Steven F. Dowdy, Ph. March 18, Kumar Sharma, M. April 1, Anthony A. Horner, M.

April 8, Robert Fitzgerald, M. April 15, Michael J. Soares, Ph. April 22, Brian Eliceiri, Ph. April 29, Jean Y. Wang, Ph. May 6, Jean Y. May 13, Karen L. Christman, Ph.