A large dataset curation and benchmark for drug target interaction

no code implementations • 30 Jan 2024 • Alex Golts, Vadim Ratner, Yoel Shoshan, Moshe Raboh, Sagi Polaczek, Michal Ozery-Flato, Daniel Shats, Liam Hazan, Sivan Ravid, Efrat Hexter

In this paper we propose a way to standardize and represent efficiently a very large dataset curated from multiple public sources, split the data into train, validation and test sets based on different meaningful strategies, and provide a concrete evaluation protocol to accomplish a benchmark.

Drug Discovery

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

1 code implementation • 5 Jul 2023 • Nicholas Heller, Fabian Isensee, Dasha Trofimova, Resha Tejpaul, Zhongchen Zhao, Huai Chen, Lisheng Wang, Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon, Yasmeen George, Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia, Mengran Wu, Zhiyang Liu, Ed Walczak, Sean McSweeney, Ranveer Vasdev, Chris Hornung, Rafat Solaiman, Jamee Schoephoerster, Bailey Abernathy, David Wu, Safa Abdulkadir, Ben Byun, Justice Spriggs, Griffin Struyk, Alexandra Austin, Ben Simpson, Michael Hagstrom, Sierra Virnig, John French, Nitin Venkatesh, Sarah Chan, Keenan Moore, Anna Jacobsen, Susan Austin, Mark Austin, Subodh Regmi, Nikolaos Papanikolopoulos, Christopher Weight

Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.

Segmentation Tumor Segmentation

Exploring the Interplay Between Colorectal Cancer Subtypes Genomic Variants and Cellular Morphology: A Deep-Learning Approach

1 code implementation • 26 Mar 2023 • Hadar Hezi, Daniel Shats, Daniel Gurevich, Yosef E. Maruvka, Moti Freiman

We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns.

Binary Classification Classification +1

Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch Embeddings

1 code implementation • 22 Aug 2022 • Daniel Shats, Hadar Hezi, Guy Shani, Yosef E. Maruvka, Moti Freiman

However, the high resolution of WSI practically prevent direct classification of the entire WSI.

Classification Contrastive Learning +2

WNTRAC: AI Assisted Tracking of Non-pharmaceutical Interventions Implemented Worldwide for COVID-19

no code implementations • 2 Sep 2020 • Parthasarathy Suryanarayanan, Ching-Huei Tsou, Ananya Poddar, Diwakar Mahajan, Bharath Dandala, Piyush Madan, Anshul Agrawal, Charles Wachira, Osebe Mogaka Samuel, Osnat Bar-Shira, Clifton Kipchirchir, Sharon Okwako, William Ogallo, Fred Otieno, Timothy Nyota, Fiona Matu, Vesna Resende Barros, Daniel Shats, Oren Kagan, Sekou Remy, Oliver Bent, Pooja Guhan, Shilpa Mahatma, Aisha Walcott-Bryant, Divya Pathak, Michal Rosen-Zvi

We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6, 000 NPIs implemented worldwide since the start of the pandemic.

Multi-Target Multiple Instance Learning for Hyperspectral Target Detection

1 code implementation • 7 Sep 2019 • Susan Meerdink, James Bocinsky, Alina Zare, Nicholas Kroeger, Connor McCurley, Daniel Shats, Paul Gader

They learn a dictionary of target signatures that optimizes detection against a background using the Adaptive Cosine Estimator (ACE) and Spectral Match Filter (SMF).

Multiple Instance Learning

Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator

no code implementations • 30 Apr 2019 • James Bocinsky, Connor McCurley, Daniel Shats, Alina Zare

Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection.