Todd Schlachter

1.9k citations
43 papers · 1.3k indexed · 1 hit paper · h-index 16

Todd Schlachter

42 papers receiving 1.2k citations

Hit Papers

Deep learning for liver tumor diagnosis part I: developme...245201920262021202350100150200

Peers

Todd Schlachter
Comparison fields: 5 of 84
  • Hepatology 634
  • Health Informatics 85
  • Radiology, Nuclear Medicine and Imaging 605
  • Oncology 260
  • Pulmonary and Respiratory Medicine 293
Replace Lynn Jeanette Savic with:
Lynn Jeanette Savic Germany
Isabel Schobert Germany
Xinming Zhao China
JingXian Shen China
Diego A.S. Toesca United States
Stefano Trebeschi Netherlands
Ying‐Shi Sun China
Gu‐Wei Ji China
Elaine Johanna Limkin France
Christopher Abbosh United Kingdom
Todd Schlachter relative to Lynn Jeanette Savic Germany Lynn Jeanette Savic's profile →
Citations per field
00.5×1.5×
Lynn Jeanette Savic · 1×
Citations per year

Countries citing papers authored by Todd Schlachter

Since Specialization
Citations

This map shows the geographic impact of Todd Schlachter's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Todd Schlachter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Todd Schlachter more than expected).

Fields of papers citing papers by Todd Schlachter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Todd Schlachter. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Todd Schlachter. The network helps show where Todd Schlachter may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Todd Schlachter, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Todd Schlachter Line = papers co-authored together Todd Schlachter links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20242
3 20233
4 20233
5 20234
6 20226
7 20227
8 20217
9 202064
10 20209
11 20195
12 20192
13 2018130
14 201810
15 201749
16 201659
17 201573
18 201437
19 201442
20 20117

About Todd Schlachter

Todd Schlachter is a scholar working on Hepatology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 43 papers that have together received 1.3k indexed citations. Recurring topics across this work include Hepatocellular Carcinoma Treatment and Prognosis (26 papers), Radiomics and Machine Learning in Medical Imaging (11 papers), MRI in cancer diagnosis (7 papers), Liver Disease and Transplantation (6 papers), Renal cell carcinoma treatment (5 papers), Tracheal and airway disorders (5 papers), Vascular Anomalies and Treatments (5 papers) and Cancer, Hypoxia, and Metabolism (4 papers). The work is most often cited by research in Hepatology (634 citations), Health Informatics (85 citations) and Radiology, Nuclear Medicine and Imaging (605 citations). Todd Schlachter has collaborated with scholars based in United States, Germany and Netherlands. Frequent co-authors include MingDe Lin, Julius Chapiro, Lynn Jeanette Savic, Isabel Schobert, James S. Duncan, Brian Letzen, Charlie Alexander Hamm, Clinton J. Wang, Jeffrey C. Weinreb and Marc Ferrante. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Clinical Cancer Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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